Climate change affects farm nitrogen loss – A Swiss case study with a dynamic farm model

AGRICULTURAL SYSTEMS Agricultural Systems 93 (2007) 191–214 www.elsevier.com/locate/agsy

Climate change affects farm nitrogen loss – A Swiss case study with a dynamic farm model S. Dueri 1, P.L. Calanca, J. Fuhrer

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Agroscope FAL Reckenholz, Air Pollution/Climate Group, Reckenholzstrasse 191, CH-8046 Zurich, Switzerland Received 12 May 2005; received in revised form 12 May 2006; accepted 29 May 2006

Abstract The response of arable crops and grasslands to climatic changes and increasing CO2 concentration has implications for the operation of farms, in particular for the management of resources such as nitrogen. A simple dynamic farm model (StellaÓ model ‘CH-Farm’) was used to analyze the shift in the ratio of N lost via leaching, denitrification and volatilization to N exported with products from dairy or arable production (here defined as relative N loss). The model was run for two types of farms typical of Swiss conditions. Growth parameters for two sequentially grown crops (winter wheat and maize) and grass were determined with the process-oriented models Pasture Simulation Model (PaSim) and CropSyst, respectively. CH-Farm was forced with two assumptions about the transient change in temperature and precipitation, and with or without CO2 effects. Relative N loss for the baseline was around 1.33 for the dairy-type farm and around 1.05 for the arable-type farm and increased progressively over the 100-year simulation period, with the largest shift in response to the dry/hot scenario. Soil N pools decreased with all scenarios, but at different rates. CO2 fertilization alleviated the effect of climate change due to increased productivity and N fixation in plants. Adjustment of the growth parameters to progressively increasing temperatures reduced the difference between farm types and positively affected relative N losses mainly through increased productivity and reduced fallow periods between crops. The results suggest that

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Corresponding author. Tel.: +41 44 3777 505; fax: +41 44 3777 201. E-mail address: [email protected] (J. Fuhrer). 1 Current address: European Commission, Joint Research Centre, Institute for Environment and Sustainability, via Fermi 1, 21020 Ispra (VA), Italy. 0308-521X/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.agsy.2006.05.005

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the impact of climate change on relative farm-level N loss depends on physiological adjustments to climatic scenarios, whereas the distribution of land between dairy and arable crop production is less important, and that simple cultivar adjustments can help to mitigate negative effects of climate change on farm-level N use. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Climate change; Nitrogen cycle; Nitrogen loss; Farm model

1. Introduction Global climate change resulting from the anthropogenic emission of greenhouse gases is expected to affect the productivity of farming systems in Europe, but the magnitude and direction will likely depend on the regional climate settings (Olesen and Bindi, 2002). Temperate northern latitudes might experience an increase in productivity, mainly due to the expansion of the growing season (Parsons et al., 2001), whereas at mid- and southern latitudes effects may be more variable, depending on the weighting of different influencing factors. Elevated CO2 concentration may stimulate biomass production in many crops, but increasing temperature, in combination with drought, could reduce yields by accelerating growth and maturation of arable crops (Monteith, 1981; Fuhrer, 2003). Modifying irrigation schemes and adjusting planting dates may, in some cases, help to overcome some of these negative effects (Donatelli et al., 2002; Zalud and Dubrovsky, 2002). While sensitivities of individual crops to climate change have received considerable attention, whole farming systems have not been the subjects of climate change research. At the farm level, climate-induced shifts in the production of one crop have implications for the cultivation of other crops or effects on grasslands for the support of animal production. In farm models, different units of production systems are linked typically through the flow of nitrogen (N) (Berntsen et al., 2003; Keating et al., 2003; Van den Bosch et al., 1998) and/or carbon (C) (Olesen et al., 2006). Fluxes of N can be separated into: (i) inputs in the form of purchased fertilizers and feedstuff, N2-fixation and atmospheric deposition; (ii) outputs with the export of products such as milk or meat, harvested crops and fodder; and (iii) losses to the environment. Ideally, N-flows into the system (i) and out with products (ii) would be balanced, and the system would be environmentally safe with respect to N (Oenema et al., 2003), while any surplus import of N may lead to N accumulation, and/or to N-losses to the atmosphere in the form of gaseous nitrogen (N2), nitrous oxide (N2O), nitrogen monoxide (NO), and ammonia (NH3), and to the hydrosphere as nitrate ðNO 3 Þ. Here, we quantify the effect of climate change on N losses as N2O, NH3, and NO 3 in relation to total farm production, i.e., relative N-loss. According to results from general circulation models (GCM) combined with downscaling procedures, the climate in the European Alps by the end of the century will be characterized by a temperature increase between 2 and 5 °C, and shifts in the seasonal distribution of rainfall and thus also in soil moisture (Jasper et al., 2004). This shift in climatic conditions could affect relative N-loss by, for instance,

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influencing the availability of soil NO 3 through increased rates of mineralization resulting in higher losses via denitrification and leaching (Smith, 1997), or through effects on crop growth and related plant uptake of N (Fuhrer, 2003). As a result, climate change could have a negative impact on N losses from farms, i.e., higher N loss relative to N export in products, which in turn would call for adjustments in N management. In this study, a simple statistical model (CH-Farm) was developed for a mixed dairy/arable farm type. CH-Farm uses relations inferred from two functional models to represent the effect of climate change on the growth of arable crops and grassland. CH-Farm is implemented with a limited amount of input variables and with simple relations describing the effect of climate on N fluxes between different compartments. The model was applied to two different farm configurations and driven with a set of simplified climate scenarios for the next 100 years. The objective was to test the sensitivity to gradually changing temperature (T) and precipitation (P) of the fluxes and pools of N, and on the relative N loss of farming systems typical of Swiss conditions.

2. Model description 2.1. Model structure CH-Farm is composed of five main subsystems with their N-pools: soil, grass, arable crops, animals (milk producing cows), and manure. N-fluxes connect these subsystems with each other and with the environment (Fig. 1). Main N-inputs are deposition and symbiotic N2-fixation from the atmosphere, import of mineral N-fertilizer, feed and concentrate. N-outputs are divided into N in exported products (milk, harvested crop and sold hay), and losses through denitrification (N2O), leaching ðNO 3 Þ and volatilization (NH3). The model was developed and implemented

N2 Fixation Purchased feed Feed supplement

PaSim

Vo latilization Denitrification Leaching

Grass

Soil

Animals

Hay

Deposition Mineral fertilizer

Milk

Crops

Manure

Volatilization

Harvested crop

Fig. 1. Model structure.

CropSyst

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using the graphical simulation program STELLAÓ Research 5.1.1. It runs with a monthly time step. 2.2. Farm characteristics The modeled farm was designed to represent typical farming systems of the Swiss Central Plateau, with mixed dairy and crop production (Swiss Farmers’ Union, 2002). The total surface area of the farm was set to 25 ha. The soil was defined as a sandyloam with 58% sand, 32% silt and 10% clay, a porosity of 0.43 m3 m3, a field capacity of 0.18 m3 m3, and a permanent wilting point of 0.09 m3 m3. The depth of the rooting zone was set to 1 m. Two types of farms reflecting different production systems were defined: Farm D with an emphasis on dairy production, and thus with a larger grassland and pasture area, and Farm C with an emphasis on crop production and thus a major fraction of cropland (Table 1). Farm area was divided into three production units: pasture, grassland and cropland. The grass subsystem included the pasture and grassland units. Milk-producing cows were assumed to graze the pastures from May to October. For grassland, a semi-intensive utilization was assumed with 3–4 cuts each year and the harvested material being stored as hay for use as feed during winter. Stocks of hey above 120 t dry matter (DM) are sold at the end of the winter season. During the summer, the pasture area not used for grazing is cut to supply feed for livestock. Cuts occur when biomass exceeds a fixed biomass threshold, as well as at the end of the growing season. Feed supplement in the form of concentrate is imported during wintertime to cover 25% of the total demand. Sensitivity analysis with other concentrate fractions yielded negligible differences in relative N loss (not shown). Fodder is imported when the stock is less than the monthly demand. The crop subsystem simulates the growth of maize and winter wheat in rotation. Temperature determines sowing and harvest dates: maize is sown when T reaches 10 °C, and the following sowing of winter wheat occurs when T drops below 10 °C. The crop is harvested when the cumulative thermal time (°C-days, Ddd) reaches the threshold for maturity, i.e., 1300 °C-days for maize and 1100 °C-days Table 1 Summary of the series of simulations using different assumptions concerning climate and farm types Scenario

WW WW+ WW WW+ HD HD+ HD HD+

Climate

Farm

DT

DP

CO2-effect

Type

Animals (number)

Cropland (ha)

Grassland (ha)

Pasture (ha)

2 2 2 2 5 5 5 5

+5% +5% +5% +5% 20% 20% 20% 20%

No Yes No Yes No Yes No Yes

Dairy (D) Dairy (D) Crop (C) Crop (C) Dairy (D) Dairy (D) Crop (C) Crop (C)

30 30 15 15 30 30 15 15

8 8 15 15 8 8 15 15

7 7 4 4 7 7 4 4

10 10 6 6 10 10 6 6

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for winter wheat. For simulations considering adaptation of crops to increasing T, it was assumed that the Ddd for the crop to be harvested was gradually increased over time from 1300 °C-days to 1800 °C-days for maize and from 1100 °C-days to 1400 °C-days for winter wheat. Livestock numbers were fixed at the beginning of the simulations (Table 1). Daily intake of each animal was set at 0.33 kg N, corresponding to 15 kg DM day1, with a concentration of 0.022 kg N kg1 DM. In the model, the animals are not allowed to accumulate N in their body: a fixed 20% of the N intake is converted to milk, and 80% are excreted. During the winter (November until the end of April) manure is stored as slurry (i.e., manure N pool), and it is used for fertilization of cropland and grassland, while during the grazing season the manure is either excreted directly on to the pasture (80%), or it is collected in the manure pool (20%). 2.3. Model inputs Weather data. The model was driven with monthly mean values of relevant meteorological parameters including P, global radiation Rs and T. The data were obtained from the Swiss Federal Office for Meteorology (MeteoSwiss). Soil water content (swc) of the current month is calculated from the balance of P, evapotranspiration (ET) and drainage (Q) by taking swc of the previous month, adding infiltration of the current month, and subtracting half of ET of the current month. If the resulting value is above field capacity (fc), the surplus of water is displaced by drainage. Thereafter the model subtracts the remaining half of ET. The minimum value of swc was set at the permanent wilting point. Following Budyko (1974) ET was estimated according to (see Fig. 2):   ET P ¼ tanh ð1Þ PET PET Here, PET is the potential ET calculated using the relation of Turc (1961):

Fig. 2. Function used for the approximation of ET.

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PET ¼

0:4  T  ðRs þ 50Þ T þ 15

ð2Þ

where the wind factor is neglected. 2.4. Plant growth functions Potential biomass accumulation of grass (DBg,pot) and crops (DBc,pot) was calculated as a function of the climatic variables using statistical relationships determined from results of simulations with either the Pasture Simulation Model (PaSim) (Riedo et al., 1998), or the crop model CropSyst (Sto¨ckle et al., 2003) (Fig. 1). The effective biomass increment (DBeff) was determined by multiplying DBpot with scalars accounting for the effects of soil water limitation (gW), high T (gT) and increasing CO2 concentration ðgeff;CO2 Þ: DBeff ¼ DBpot  gW  gT  geff;CO2

ð3Þ

For grass, DBg,pot was calculated from the difference between potential growth and turnover: DBg;pot ¼ max½ð30  fg  K  Bg;eff Þ; 0

ð4Þ 1

where K is a turnover rate equal to 0.75 month , following the setup of PaSim. The potential growth of grass, fg, was described as a function of Rs and T. The functional dependence was derived by examining the behavior of daily grass growth, as simulated with PaSim, for ranges of T between 1 and 30 °C, and of Rs between 50 and 250 W m2. The resulting surface of possible growth rates was approximated by, fg ðRs; T Þ ¼ ðA1 þ A2  RsÞ  f1  eðmnRsÞT b1 þ ðm  n  RsÞ  T 2

þ 0:5  ððm  n  RsÞ  T Þ cg

ð5Þ

with values of A1 = 0.00207, A2 = 0.0000311, m = 0.3574 and n = 0.00038. For maize and winter wheat, DBc,pot was represented as a function of the monthly mean of Rs and total thermal time Ddd: DBc;pot ¼ c  Rs  Ddd

ð6Þ

The coefficient c was determined from the results of simulations with CropSyst with irrigation. The value of the coefficient depends on the crop and c = 0.062 for maize, while for winter wheat we distinguish between a first phase (Ddd < 750) where c = 0.0415 and a second phase (Ddd > 750) where c = 0.026. To compute Ddd it was assumed that the seasonal course of T is approximated with a sinusoidal function, T ðDÞ ¼ T M þ AT sinðxD  uÞ

ð7Þ

where TM is annual mean T, AT is the amplitude of the annual cycle, x = 2p/360 and D is the day of the year (all years are assumed to have a length of 360 days). This approximation is justified by the data (not shown). Given Eq. (7) the thermal time

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accumulation was calculated analytically by integration of the difference between T and a reference temperature Tbase: Ddd ¼

Z

DE

ðT ðDÞ  T base Þ dD

DB

¼ ðT M  T base ÞðDE  DB Þ 

AT ðcosðxDE  uÞ  cosðxDB  uÞÞ x

ð8Þ

Tbase was set to 6 °C for maize and 2 °C for winter wheat (Lang and Mueller, 1999). The lower limit of integration, DB, is either the first day of the month or the sowing date, or for winter wheat, the date in spring when T = Tbase again. The upper limit of integration, DE, is either the last day of the month or the harvest date, or for winter wheat, the date in autumn when T < Tbase. The development of maize and winter wheat follows different patterns. In CropSyst the early development of winter wheat is affected by the vernalization and photoperiod, which are represented each by factors scaled between 0 and 1. However, the results of simulations with mean annual T increased and decreased by 3 °C (not presented) indicated that in practice vernalization affects the growth during a maximum of only six days. Therefore, this factor was neglected, and the monthly accumulation of thermal time during the winter period was calculated according to: Dddver ¼ Ddd  dv

ð9Þ

where the factor dv accounting for the limiting effects of photoperiod was calculated for the 15th of each month using the equation described in CropSyst: dv ¼

daylength  daylengthif daylengthins  daylengthif

ð10Þ

In Eq. (10) daylengthif = 8 is the day length below which flowering is inhibited, daylengthins = 20 is the day length above which plants are insensitive, and the variable daylength is calculated using the sun declination. Soil water limitation. The reduction factor gw accounting for the effects of water limitation was set equal to the ‘wetness index’ ET/PET, as given by Eq. (2). Temperature limitation. High T accelerates crop development and increases crop respiration, resulting in reduced yield (Ro¨tter and Van de Geijn, 1999). According to simulations with CropSyst, the relative decrease in yield was proportional to the reduction of the length of the growth period. The reduction factor accounting for the effects of elevated T, gT, scaled between [0, 1], was therefore computed according to:   Dtact gT ¼ ð11Þ Dtopt where an optimal growth period Dtopt of 80 days was estimated with the help of CropSyst for the arable crops. CO2 effect. The model represents the effect of the CO2 stimulation of yield by the factor geff;CO2 , which is a function of the CO2 concentration:

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geff;CO2 ¼ g2CO2 

  ½CO2   ½CO2 init þ1 ½CO2 init

ð12Þ

In the scenarios with changing CO2 concentrations ([CO2]), the initial concentration of 370 ppm for the year 2000 ([CO2]init) was linearly increased to 740 ppm by 2100. The specific response of crops to elevated CO2 concentrations was accounted for by the coefficient g2CO2 . For wheat (C3 crop) it was set at 0.2, based on experimental results (cf. Kimball et al., 2002). For maize (C4 crop), g2CO2 was taken as a function of gw and was set at 0.2 for gw < 0.5, and decreased linearly to 0 for gw between 0.5 and 1 (cf. Kimball et al., 2002). 2.5. N-pools and fluxes Mineralization. Organic N in soil becomes available to plants after mineralization under aerobic conditions. This process is the sum of ammonification converting organic N to ammonium ðNHþ 4 Þ, and subsequent nitrification, i.e., oxidation of  NHþ to NO . Therefore, the available N pool (Navail) is defined as the sum of 4 3  NHþ and NO . 3 4 In CH-Farm the soil subsystem is subdivided into three organic N pools: slow pool (humus), fast pool (litter, microbial biomass) and N-available pool (NO 3, NHþ ). The fast pool has a turnover time of 1–2 years, while the slow pool is more 4 recalcitrant and has a turnover time of 20–40 years. These pools are connected by mineralization fluxes with different rates for the fast (Minf) and slow (Mins) pools (see Fig. 3). N input to the fast and slow organic pools is from applied manure and soil incorporation of plant residues. The organic N in manure was partitioned between fast and slow pools at a ratio of 3:2, based on Swiss fertilization guidelines (Walther et al., 2001). To calculate the N flux associated with incorporation of plant residues into the soil organic N-pools, it was assumed that the residue fraction of total DM is 35% in maize and 25% in winter wheat (Walther et al., 2001). Residues are then partitioned between the fast (Norg,f) and slow (Norg,s) pools, assuming that they are composed of approximately 25% fast C and 75% slow C, and assuming a constant C:N Turnover 1-2 y

Min f

N org, fast

Manure Grass Residues .

Min f

N avail

Crop Residues N org, slow

Turnover 20-40 y

Min s

Fig. 3. Diagram showing the mineralization fluxes in the model.

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ratio of about 7 for fast and 20 for slow decomposable organic inputs (Mueller, 2000). The relations used to calculate Minf and Mins were determined from the equation of Kirschbaum (2000), as described in Paul et al. (2003), and are functions of the concentration of Norg, the potential mineralization rate Minpot, and depend on two factors accounting for effects of T (kT), and swc (kW): Minf ¼ k T  k W  Minpot;f  N org;f

ð13Þ

Mins ¼ k T  k W  Minpot;s  N org;s 1

ð14Þ 1

Minpot was set at 0.3 month and 0.032 month for the fast and slow pool, respectively. In Eqs. (13) and (14) kT is the dimensionless parameter accounting for the effect of soil T (Tsoil) on microbial activity (Paul et al., 2003):    T soil  40 k T ¼ exp 3:36  ð15Þ ; T soil < 40  C T soil þ 31:79 where kW is a scalar increasing from 0 at the permanent wilting point to 1 at field capacity accounting for the effect of swc on mineralization, following Riedo et al. (1998). To simplify, Tsoil was set equal to T. N uptake. The potential uptake of N by the plants is assumed to be proportional to the effective growth, but limited by the availability of mineral N, Navail. If Navail exceeds N demand, then N uptake is the effective biomass growth multiplied by a conversion factor of 0.022 kg N kg1 DM for grass, 0.013 kg N kg1 DM for maize and 0.02 kg N kg1 DM for winter wheat. If Navail falls short of N demand, N uptake is equal to the available N. N-leaching. Leaching (Leach) of NO 3 is the product of the drainage, Q, concentration of Navail and a land-use specific leaching factor cleach: Leach ¼ cleach  Q  N avail

ð16Þ

Lysimeter experiments with different crop rotations on a loamy sand have shown that average leaching rates are of 10–20 kg N ha1 year1 for permanent grassland and 40–80 kg N ha1 year1 for crops (Stauffer and Spiess, 2001). To adhere to these values, cleach was set to 0.15 for pasture and grassland and 0.4 for cropland. Denitrification. Denitrification (Denitr) is represented as the product of Navail, scalars accounting for effects of soil moisture (jW) and T (kT), and a land-use specific potential denitrification factor cdenitr Denitr ¼ cdenitr  jW  k T  N avail

ð17Þ

where kT is approximated by the same function as used for mineralization, whereas jW is a function of the ratio of swc and fc being 0 for swc 6 0.5fc, and linearly increasing to 1 for swc > 0.5fc (Aulakh et al., 1992). cdenitr was set at 0.25 for grassland and pasture and 0.3 for cropland to fit to observed annual denitrification rates in the range of 20–30 kg N ha1 (Schmid et al., 2000). N2 fixation. 80% of the N demand of clover is met through biological N2 fixation (Boller and No¨sberger, 1987). Assuming a typical clover fraction of 30% of the herbage biomass (Schmid et al., 2000), fixation supplies 25% of the total N

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demand of the pasture and grassland units. This value was kept constant in all simulations. Application of manure and mineral fertilizer. Frequency and amount of N application in the form of manure or mineral fertilizer were set according to national guidelines (Walther et al., 2001): (a) 25 kg ha1 of N is applied to grassland in March and after each cut, except after the final cut; (b) a total of 110 kg N ha1 is applied to maize in April and May, while winter wheat is fertilized in October, March and April with a total of 150 kg N ha1. The guidelines refer to readily available N. In manure, about 40% of the nitrogen is not readily mineralized. For this reason, 1.65 was taken as a multiplier to obtain corresponding rates of manure application. N-volatilization. The model distinguishes two different volatilization fluxes of ammonia, one from the manure pool and the other from the field. The volatilization from the manure pool depends on the surface of the reservoir, which was taken as 6 m2 per animal, representing Swiss farming systems (Menzi et al., 1997). The temperature dependence of the volatilization rate, Vol in kg N m2 month1, was adapted from the relation presented in Berg et al. (2003):  0:06 þ 0:006  T if 0 6 T < 15  C Vol ¼ ð18Þ 0:15 þ 0:012  ðT  15Þ if 15 6 T < 40  C The temperature dependency for the volatilization from the field was derived from the results presented in Huijsmans et al. (2001) and increases linearly between 0 and 35 °C and gives a maximum rate of 45% of total N in manure at T = 34 °C. Atmospheric deposition. Atmospheric N deposition was set at 25 kg N ha1 year1 (Rihm, 1996). 2.6. Model testing Simulated swc was compared with measurements in a grassland soil in the Rietholzbach area in eastern Switzerland between 1981 and 2000 (Fig. 4). The data refer to an assessment of the water balance carried out from 1976 to present by the Swiss Federal Institute of Technology, ETH Zurich (http://www.iac.ethz.ch/research/rietholzbach/). For the simulation, the depth of the rooting zone was set to 1100 mm, the porosity to 0.65, corresponding to a water depth of 715 mm, and the degree of saturation at fc to 0.8 and at the permanent wilting point to 0.3, corresponding to a water depth of 572 and 215 mm, respectively (Calanca, 2004). The comparison indicates that the simple representation of the soil hydrological processes in CH-Farm is able to capture the seasonal course and inter-annual fluctuations of swc and drainage, despite the fact hat swc is limited to fc, and occasional very low swc is underestimated by the model. Only few data on farm level fluxes and stocks of N, as represented in the model, are available for Switzerland to assess the performance of the model as a whole, but partial testing of individual modules was possible. Crop and grass productivity simulated by the model agreed well with observed average values for the years 1981– 2000 for 1985–2001 (Swiss National Farmers’ Union, 2002) (Table 2). Estimates

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Fig. 4. Test of the soil hydrological model by comparing the simulation with measured values (straight line = measurements, dotted line = simulations).

Table 2 Comparison of average values for annual yield obtained by simulation with observed national averages for Switzerland

Maize yield Winter wheat yield Milk production Grassland yield (semi-intensive management) Pasture yield (semi-intensive management)

1

(t ha ) (t ha1) (t year1 animal1) (t DM ha1 year1) (t DM ha1 year1)

Simulation

Swiss national average

8.7 4.7 4.7 6.8 6.2

7.9–9.6 5.3–6.0 4.7–5.5 7.5–10.0 6.5–8.5

of mean N-losses ðNH3 –N þ N2 O–N þ NO 3 –NÞ based on census data for over 50 different Swiss farms (year 1998) amounted to 81 kg N ha1 year1 (Rossier and Gaillard, 2004), which compared favorably with the values of 77 and 76 kg N ha1 year1 for the dairy and arable farm types in the control run. The ratio between N exported in products and total N input to the system was around 0.4–0.5. This is higher than the value for Swiss agriculture as a whole, which is estimated at about 0.2–0.3 (Spiess, 1999; Flisch et al., 2001). It should be noted, however, that the

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latter value refers to a much wider range of farming systems than considered in the present analysis, and that the present analysis does not include all N fluxes, for instance fluxes of N2 are excluded for the balance.

3. Simulations Simulations were performed with two climate scenarios with the assumptions of crops being either non-adapted or adapted to increasing T (Table 1). The scenarios were defined by linear trends in T and P starting in 2001 and ending in 2100. The ‘warm-wet’ (WW) scenario was characterized by a final T increase of +2 °C and a final increase in summertime (April to September) P by +5%, relative to the current regional climate. The ‘hot-dry’ (HD) scenario was defined by an increase in T by +5 °C and a decrease in summertime P by 20%. Both scenarios assumed an increase in wintertime (October to March) P by +10%. For both climate scenarios, simulations were carried out with (+) or without () including the CO2 fertilization effect. Atmospheric CO2 concentration was assumed to linearly increase from currently 370–740 ppm at the end of the 21st century. This is comparable to the increase proposed in the SRES IS92a (700 ppm by 2100) and A1B (700 ppm by 2100) scenarios by the Intergovernmental Panel on Climate Change (Houghton et al., 2001), but somewhat lower than the increase assumed in the A2 scenario (850 ppm by 2100). The two scenarios for T and P were constructed to represent the range of the climate change projections obtained for the Alpine region from the ensemble of regional climate simulations produced in the framework of the European project PRUDENCE (data were compiled by C. Frei, ETH Zurich, personal communication). The combination of the climate/CO2 scenarios and two farm types led to a total of eight simulation runs (Table 1). All simulations started in 1981. The first 20 years defined the baseline and were run with observed meteorological data for the station of Zurich. This station was considered as representative for the climatic conditions of the Swiss Plateau. Thereafter, the series were repeated 5 times applying linear shifts in T and P as described above until 2100 when the final values of scenario change were reached. N-pools of the pasture, grassland and cropland units were initialized with values in the range typical for Swiss agriculture (see Schmid et al., 2000) given in Table 3.

Table 3 Initial N concentration in three soil pools for pasture, grassland and cropland (N-available pool, fast organic pool and slow organic pool) N pool

Pasture (kg N ha1)

Grassland (kg N ha1)

Cropland (kg N ha1)

Available pool Fast organic pool Slow organic pool

50 120 2600

90 120 2500

100 100 2200

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Running the model for 120 years with the baseline climate data by repeating 6 times the 20-year data confirmed the long-term stability of the pools under present condition.

4. Results 4.1. Simulations with current crop-cultivars (without adaptation) For a first set of simulations, the two farming systems were left unchanged over time, with fixed fractions of arable land, grassland and pasture, constant number of animals, and the same parameters of the two types of crops set for current climate. These fixed systems were examined for their sensitivity to the climate change scenarios. Compared to average values obtained for the period 1981–2000 (baseline), the scenarios caused relative changes in productivity, N pools and N-fluxes per unit of farm area, which differed quantitatively between the two farm types (Table 4). Inputs increased in both types, with largest relative effects of the different scenarios on biological N2 fixation. On the output side, the scenarios reduced N-leaching but increased N2O loss through denitrification. Grassland productivity increased in all simulations, while in seven out of eight simulations crop productivity decreased. These trends were due to the raise in T, which extended the period of grass growth leading to larger annual production. Conversely, increased T accelerated crop development, and consequently reduced the yield of maize and winter wheat. Table 4 also shows that the more extreme HD scenario caused a higher level of grass production than the milder WW scenario. Grass growth seemed to be more strongly affected by the positive effect of T than by the negative effects of increasing swc limitation. The effect of increasing CO2 was to enhance plant N-uptake, which in turn lowered the increase in N-losses. During the first 50 years of simulation, a trend towards increasing leaching rates was found, which could be explained by enhanced N-availability in soils, while a decrease in P reduced drainage and the amount of N leached during the second half of the simulation period (not shown). In all scenarios, increasing mineralization rates were responsible for a decrease in the soil N-pool, which was more pronounced near the end of the simulation period (Fig. 5). Relative N loss, as expressed as the ratio of N lost from the systems to N exported in products based on annual data, was initially 1.33 for D-farm and around 1 for the C farm. With time, it increased in almost every scenario (Fig. 6), with the exception of the WW+ scenario. In the latter case, we observed a steady ratio for the C-farm, and a slight decrease for the D-farm. The decrease followed from the fact that sustained productivity of the grassland units mainly due to T-induced extension of the growing period more than compensated for the slight increase in N losses. Conversely, the increase in relative N-loss in the other simulations was related to the decline in crop productivity, most strongly expressed with HD, leading to higher levels of N available for NH3 and N2O losses. The results also indicated that relative N-loss was generally lower in the WW than in the respective HD scenario. The

204

Dairy farm (D) 1

Pool (kg N ha )

Soil N-pool 1

1

WW

HD

WW+

HD+

Baseline

WW

HD

WW+

HD+

3177

14

32

13

31

2149

15

33

13

31

Mineralization

135

+4

+7

+6

+9

90

+1

+3

+4

+7

Inputs (kg N ha1 year1)

N2 fixation Manure + mineral fertilizer

31 154

+14 +2

+30 +3

+17 +5

+33 +5

18 150

+13 +1

+29 +1

+17 +3

+31 +2

Outputs (kg N ha1 year1)

Crop harvest Grass harvest

33 66

9 +16

21 +24

+1 +31

11 +40

62 38

10 +16

21 +23

2 +29

11 +35

Losses (kg N ha1 year1)

Leaching Denitrification NH3 volatilization

38 32 7

0 +5 +12

13 +39 +30

10 12 +12

21 +18 +30

46 26 4

+6 +8 +12

4 +59 +30

3 9 +12

12 +36 +30

Internal flux (kg N ha

year )

Crop farm (C)

Baseline

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Table 4 Baseline data and relative changes (in %) in the soil N-pool and in the soil related N-fluxes for simulation without cultivar adaptation

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205

Fig. 5. Transient development of the mean soil N-pool.

curves displayed in Fig. 6 also show that scenarios, which included the CO2 fertilization effect, led to lower relative N-losses than those, which did not; this was due to higher plant biomass production and N-uptake with CO2 fertilization. 4.2. Simulations with adaptation of crop-cultivars to the new climate In a second set of simulations, it was assumed that the cultivars used would be adapted to higher temperatures and a longer growing season through a steadily increasing Ddd, i.e., increasing thermal requirement for plant growth. With this assumption, the simulations revealed higher crop productivity associated with a decrease in N-leaching and fluxes of N2O for all scenarios (Table 5). Again, the HD scenario caused higher N2O-losses compared to the WW scenario, but lower rates of N-leaching in both farm types. The enhancement of crop productivity was strongest with elevated CO2. The positive effect of crop adaptation on N losses was mainly due to and to increased N-fixation in plant biomass. NH3 volatilization was unaffected by the use of adapted cultivars. Introduction of adapted cultivars decreased relative N loss for scenarios with CO2 fertilization included, while for the other scenarios it remained at a more or less

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Fig. 6. Transient development of relative N loss (i.e., N lost/N exported with products) for the two model farming systems without crop cultivar adaptation.

constant level (Fig. 7). The decrease was caused by enhanced crop and grass productivity. Conversely, in the scenarios without CO2 effect, there was only a slight increase in crop productivity. Relative N loss for the D-farm was lower with WW than with the HD scenario, whereas for the C-farm, differences between farm types were small.

5. Discussion Environmental impacts caused by losses of N from agricultural systems are well known (Galloway et al., 2003), but little attention has been paid so far to the future trends under changing climate and increasing CO2. Farms are the places where much of the adaptation in response to risks from climate change can take place, and in the case of N, could help to mitigate increased N losses. Studies of climate change impacts at the whole-farm levels require an approach that takes account of the complexities of biophysical processes, management and land use at individual farms (Rivington et al., 2005), but the many interactions between the different farm components complicate the projection of potential effects of climate change at this level.

Dairy farm (D) 1

Pool (kg N ha )

Soil N-pool 1

1

WW

HD

WW+

HD+

Baseline

WW

HD

WW+

HD+

3177

13

31

12

30

2149

13

29

11

27

Mineralization

135

+5

+9

+7

+12

90

+4

+10

+7

+14

Inputs (kg N ha1 year1)

N2 fixation Manure + mineral fertilizer

31 154

+14 +2

+30 +3

+17 +5

+33 +5

18 150

+13 +1

+29 +1

+17 +3

+31 +2

Outputs (kg N ha1 year1)

Crop harvest Grass harvest

33 66

+5 +16

+3 +24

+15 +31

+15 +40

62 38

+3 +16

+3 +23

+11 +29

+13 +35

Losses (kg N ha1 year1)

Leaching Denitrification NH3 volatilization

38 32 7

9 0 +12

25 +29 +29

19 17 +12

34 +6 +29

46 26 4

7 2 +12

22 +33 +28

17 19 +12

31 +11 +27

Internal flux (kg N ha

year )

Crop farm (C)

Baseline

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Table 5 Baseline data and relative changes (in %) in the soil N-pool and in the soil related N-fluxes for the simulation with cultivar adaptation

207

208

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Fig. 7. Transient development of relative N loss (i.e., N lost/N exported with products) for the two model farming systems with crop cultivar adaptation.

There are numerous ways by which climate change affects the soil/crop/livestock processes. In this study, we focused on the implications of climate change for N flows within two types of model farms, which are representative of Swiss conditions. Specifically, we estimated differences in the amount of environmentally relevant N lost per unit of N exported in products (i.e., relative N loss) between different climatic trends. For this sensitivity analysis, a model was developed that takes account of the seasonal dynamics of the climatic drivers, and in both system of N stocks and N flows. The model is kept simple as it does not consider effects of possible technological developments, nor does it contain a detailed representations of processes related to the consumption of forages by the animals, or to the handling and storage of manure. Results from simulations without adaptive changes in plant characteristics reveal changes in relative N loss over time, and differences between the two farm types. In the baseline simulation, relative N loss is larger in the case of the farm dominated by dairy production, in agreement with data from farm surveys (Rossier and Gaillard, 2004). In the crop-dominated farm, increasing T in the scenario runs induces a shift in crop sowing dates, while at the same time accelerating crop growth. This results in extended fallow periods in the rotation, and thus reduced N-fixation in plants. The

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209

increased potential for relative N losses is reduced with elevated CO2 because of increased productivity and N uptake (Fig. 6). The dairy farm is less sensitive to the extreme HD scenario; the prescribed reduction of summertime P by 20% by the end of the 100-year simulation in the HD scenarios seemed not sufficient to have a strong effect on grass production, possibly because the initial climate was relatively humid. Additional runs with different rainfall scenarios revealed that summertime P would have to decrease by as much as 40–50% in order to have a major effect on grassland yield (results not presented). Such a reduction is in the order of magnitude observed in Switzerland during the extreme summer 2003 (Keller and Fuhrer, 2004), and it represents an extreme climate change and soil moisture projection (Jasper et al., 2006). Adaptation to climate change at the farm level is likely to occur in a variety of ways, including introduction of new crops or varieties, shifts in cropping sequences, altered resource management, introduction of new technologies, etc. (Olesen and Bindi, 2002). In simulations with the simple assumption of crop varieties becoming progressively adapted to increasing T, the differences in relative N loss between farm types becomes smaller, and changes over time relative to the baseline are small, with a slight decreasing trend. Switching to adapted cultivars reduces the time of fallow between crops and thus the amount of soil N available for denitrification and leaching. In combination with effects of elevated CO2, relative N loss decreases most over time due to the combined beneficial effects of a longer growing period and increased productivity. However, the effect of CO2 may be overestimated since future cultivars may be limited in their capacity to assimilate extra CO2 and thus be less responsive to increasing CO2 than expected (cf. Pritchard and Amthor, 2005). Nevertheless, the results indicate that simple adaptation in terms of switching crop cultivars can help to increase the N-efficiency of the system by increasing productivity and decreasing N losses. Adaptive measures would also need to ensure the long-term sustainability of the production systems, such as crop diversification to reduce farm risks (Bradshaw et al., 2004). As noted above, in all scenarios the soil N-pool diminished during the second half of the simulation period. Although in our case the decrease did not affect grass and crop productivity, the fate of the soil N-pool may become of relevance if changes in T and P would be stronger than proposed by the HD scenario. Compared to the WW scenario, the warmer HD scenario caused higher N2O loss, but lower rates of N-leaching in both farm types. This is in line with the results of Olesen et al. (2004) who found increasing N2O emissions from crop rotations with increasing T on loamy sand in Denmark using a soil-crop atmosphere system model (DAISY model). With the HD scenario, leaching is less because of smaller P, and more N is available N to be converted to N2O. Denitrification occurs under anaerobic conditions, and field measurements indicate lower emission factors for N2O for fertilized grasslands during a warm/dry year (Flechard et al., 2005). Apparently, swc in the HD simulations remains sufficiently high for denitrification to occur. With increasing grassland productivity in a warmer world, one could assume that the use of concentrates would decline, rather than fodder sales to increase. But extra simulations with different assumption about the fraction of concentrates used did not

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yield a significant difference in relative N loss by the end of the simulation period (not shown), and hence manipulating the use of concentrates appears not to be an effective measure to improve farm-level N management. What is more effective is the maintenance of high rates of N-fixation in crops by using climate-adapted cultivars, which minimizes fallow periods between crops. The results clearly show that more productive farm systems tend to have lower relative N losses, thus indicating higher N efficiency (i.e., output/input balance, Olesen et al., 2006). In contrast, in the simulations by Olesen et al. (2006), higher farm production intensity increased N surplus and lowered N efficiency. The difference is due to the fact that in the present simulation, livestock density remains unchanged, and N-input is shifted to account for climate-induced changes in crop and grassland productivity. The lack of consideration of dynamic adjustments in land allocation, livestock density or fertilizer application rates presents an important limitation of the current model simulations, the importance of these adjustments needs further investigations with a model expanded along the lines suggested by Rivington et al. (2005). Further limitations of the present sensitivity study is the lack of a full assessment of environmental costs and benefits, which would need to consider the total emission of greenhouse gases related to animal husbandry, including emission of methane and carbon dioxide, as well as indirect emissions (Olesen et al., 2006). Finally, the scenarios presented here do not consider the possibility of an increase in the frequency of extreme heat waves during the latter part of this century, as suggested for central Europe by recent simulations with regional climate models (Beniston, 2004; Scha¨r et al., 2004). Increased inter-annual variability could lead to larger yield losses than the scenarios with a gradual change like the ones represented in our simulations (Semenov et al., 1996). If heat waves such as the one that struck the European continent in summer 2003 should occur more frequently in the future, climate change could have a negative impact also on the productivity of grasslands and pastures, with associated negative effects on N fixation in biomass and N loss to the environment.

6. Conclusions Simulations revealed that the N-cycle of mixed farming systems is sensitive to gradually increasing T and decreasing P. In systems more heavily relying on crop production, warming causes an increasing fraction of N imported into the system to be lost to the environment in the form of NH3, N2O and NO 3 , at the expense of N exported with products. Systems with more dairy production tend to benefit from the longer growing periods and sustained productivity of grasslands and pastures, leading to larger N fixation in products and smaller relative N losses. The difference between systems is reduced and shifts in relative N loss with changing T and P become smaller when fallow periods between crops are reduced by simply replacing current cultivars of crops with cultivars characterized by longer growing periods due to progressively increasing thermal demand for plant development.

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Acknowledgements This study was carried out in the framework of the National Center of Competence in Research on Climate (NCCR Climate funded by the Swiss National Science Foundation). The Swiss Federal Office for Meteorology and the Institute for Atmospheric and Climate Research of the Swiss Federal Institute of Technology (IACETH) are acknowledged for providing access to their databases. The regional climate simulations have been provided through the PRUDENCE data archive, funded by the EU through contract EVK2-CT2001-00132.

Appendix A. Symbols and units in CH-Farm AT c cleach cdenitr DBpot DBg,pot,

amplitude of the annual temperature cycle (°C) coefficient of crop growth (kg DM W1 °C1 month1) land-use specific leaching factor () land-use dependent potential denitrification factor () potential rate of biomass accumulation DBc,pot potential grass and crop growth, respectively (kg DM m2 month1) DBeff effective growth (kg DM m2 month1) Ddd total thermal time (°C days) dttver daily thermal time during vernalization (°C days) dv photoperiod factor () ET evapotranspiration (mm month1) fg daily potential grass growth (kg DM m2 day1) fc field capacity (mm H2O m3) g2CO2 coefficient of stimulation of biomass by doubled CO2 () geff;CO2 coefficient of effective CO2 stimulation () gT temperature limitation factor () gW soil water limitation factor () K turnover rate of grass (month1) kT coefficient of soil temperature effect on mineralization () kW coefficient of soil water content effect on mineralization () jW factor of soil moisture effect on denitrification () 1 þ Navail pool size of nitrogen available in the soil ðNO 3 ; NH4 Þ (kg N ha ) 1 Norg,f, Norg,s fast and slow organic N pools, respectively (kg N ha ) Minpot potential mineralization rate (month1) Minf, Mins mineralization rate of fast and slow organic N pools, respectively (month1) P precipitation (mm month1) PET potential evapotranspiration (mm month1) Q drainage (mm month1) Rs global radiation (W m2) swc monthly mean soil water content (mm H2O)

212

T Tbase TM Tsoil Volpool

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temperature (°C) base temperature for plant growth (°C) annual mean temperature (°C) soil temperature (°C) NH3 volatilization factor (kg N m2 month1)

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