Supplemental material for
Responses of nitrous oxide emissions from crop rotation systems to four projected future climate change scenarios on a black Vertosol in subtropical Australia
Yong Li 1,2, De Li Liu3, Graeme Schwenke4, Bin Wang3,5, Ian Macadam6, Weijin Wang7, Guangdi Li3, Ram C Dalal7
1 Changsha Research Station for Agricultural & Environmental Monitoring and Key Laboratory of Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Hunan 410125, China
2 Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Victoria 3010, Australia
3 NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Pine Gully Road, Wagga Wagga, NSW 2650, Australia
4 NSW Department of Primary Industries, 4 Marsden Park Road, Tamworth, NSW 2340, Australia
5 School of Life Sciences, Faculty of Science, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia
6 Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Australia. Now at Met Office, UK
7 Department of Science, Information Technology and Innovation, Brisbane, PO Box 5078, QLD 4001, Australia
Weather data downscaling
Since the monthly GCM data were not suitable as direct input into site-scale WNMM simulations, we downscaled them to site-scale daily data using the weather-generator based statistical downscaling method of Liu and Zuo (2012). This method corrects some of the biases inherent to GCM simulations and has been widely applied in recent climate change impact studies (Anwar et al. 2015; Liu et al. 2014; Wang et al. 2015). Site-scale biophysical models usually require unbiased time series of site-scale meteorological data to produce reliable simulation outputs. For example, Macadam et al. (2016) demonstrated the need for unbiased input to a site–scale agricultural model, even when future changes in simulation outputs, and not absolute values of outputs are of interest.
We downscaled gridded monthly temperature and rainfall data from the 15 GCMs to daily data for Tamworth for 1952–2099. The downscaling procedure began with spatial interpolation of the monthly gridded data to Tamworth using an inverse distance-weighted method, followed by a bias correction towards historical climate observations for the site (Liu and Zuo 2012). Daily climate data for each location were then generated from the monthly data using a modified version of the WGEN stochastic weather generator (Richardson and Wright 1984). The parameters required to drive WGEN were derived from the monthly GCM data using relationships between monthly and daily data derived from observations, as described in Liu and Zuo (2012).
References
Anwar MR, Liu DL, Farquharson R, Macadam I, Abadi A, Finlayson J, Wang B, Ramilan T (2015) Climate change impacts on phenology and yields of five broadacre crops at four climatologically distinct locations in Australia. Agricultural Systems 132:133–144
Liu DL, Anwar MR, O'Leary G, Conyers MK (2014) Managing wheat stubble as an effective approach to sequester soil carbon in a semi-arid environment: Spatial modelling. Geoderma 214:50–61
Liu DL, Zuo H (2012) Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia. Climatic change 115(3–4):629–666
Macadam I, Argüeso D, Evans JP, Liu DL, Pitman AJ (2016) The effect of bias correction and climate model resolution on wheat simulations forced with a Regional Climate Model ensemble. International Journal of Climatology 36(14): 4577–4591
Richardson CW, Wright DA (1984) WGEN: A model for generating daily weather variables. USDA ARS, USA
Wang B, Liu DL, Asseng S, Macadam I, Yu Q (2015) Impact of climate change on wheat flowering time in eastern Australia. Agricultural and Forest Meteorology 209:11–21
Table S1 Details of four cropping systems applied at Tamworth during 2009–2012. Dates are expressed as day of year, and N fertilizers were applied on the days of sowing.
Treatment / Rotation / Sowing date / Fertilization / Harvest dateT1CaWB / Canola (2009)
Wheat (2010)
Barley (2011) / 170
208
178 / 80 kg ha-1 N
80 kg ha-1 N
60 kg ha-1 N / 330
349
350
T3CpWB / Chickpea (2009)
Wheat (2010)
Barley (2011) / 170
208
178 / 80 kg ha-1 N / 330
349
350
T4CpWCp / Chickpea (2009)
Wheat (2010)
Chickpea (2011) / 170
208
178 / 330
349
350
T5CpS / Chickpea (2009)
Sorghum (2010-11) / 170
314 / 40 kg ha-1 N / 330
69
Table S2 Simulated annual rainfall and mean temperature change for 2015–2056 and 2057–2098 under four RCP scenarios relative to the baseline period (1952–2014) from the 15 GCMs used in this study.
GCM / Rainfall change (%) / Tmean change (˚C)Name / Abbrev. / RCP2.6 / RCP4.5 / RCP6.0 / RCP8.5 / RCP2.6 / RCP4.5 / RCP6.0 / RCP8.5
2015–2056 / 2015–2056
bcc-csm1-1 / BC1 / -4.04 / 2.76 / -5.35 / -1.04 / 1.24 / 1.51 / 1.43 / 1.74
bcc-csm1-1-m / BC2 / 24.08 / 5.46 / -0.04 / 17.76 / 1.07 / 1.46 / 1.39 / 1.67
CCSM4 / CCS / 5.70 / 6.38 / -0.92 / 13.55 / 1.33 / 1.53 / 1.42 / 1.81
CSIRO-Mk3-6-0 / CSI / 7.81 / 12.73 / 5.57 / 5.69 / 1.21 / 1.19 / 0.91 / 1.46
FIO-ESM / FIO / 11.28 / 3.44 / 15.49 / 22.15 / 1.56 / 2.00 / 1.77 / 2.20
GFDL-ESM2G / GE3 / 10.36 / -1.22 / 10.87 / -5.43 / 0.50 / 0.76 / 0.75 / 1.12
GISS-E2-R / GF3 / -2.66 / -9.78 / -11.18 / -11.81 / 0.79 / 1.00 / 0.93 / 1.15
HadGEM2-AO / Ha5 / -15.61 / -8.01 / -13.58 / -15.33 / 1.23 / 1.67 / 1.32 / 1.64
IPSL-CM5A-MR / IP2 / 16.65 / 18.23 / 24.84 / 20.56 / 0.84 / 1.22 / 1.18 / 1.45
MIROC5 / MI2 / 8.16 / 6.59 / 3.24 / 9.11 / 1.52 / 1.55 / 1.40 / 1.67
MIROC-ESM / MI3 / 13.05 / 11.83 / 8.35 / 22.92 / 1.68 / 1.95 / 1.64 / 1.88
MIROC-ESM-CHEM / MI4 / 11.11 / 13.18 / 15.67 / 19.94 / 1.83 / 1.65 / 1.73 / 1.82
MRI-CGCM3 / MR3 / -0.79 / 9.69 / 4.97 / 7.73 / 0.72 / 1.03 / 0.79 / 1.16
NorESM1-M / NE1 / 3.52 / 9.25 / 1.88 / 7.95 / 1.24 / 1.41 / 1.27 / 1.57
NorESM1-ME / NE2 / 5.89 / 11.75 / 6.99 / 12.77 / 1.04 / 1.21 / 1.11 / 1.55
Mean / 6.30 / 6.15 / 4.45 / 8.43 / 1.19 / 1.41 / 1.27 / 1.59
Country / 2057–2098 / 2057–2098
China / BC1 / -3.33 / -3.89 / -0.68 / -9.37 / 1.32 / 2.21 / 2.49 / 3.99
China / BC2 / 3.44 / -1.33 / 0.72 / 11.59 / 1.25 / 1.81 / 2.21 / 3.46
USA / CCS / 9.01 / 4.22 / 7.52 / 23.26 / 1.34 / 2.32 / 2.72 / 4.19
Australia / CSI / -14.25 / -16.22 / -5.69 / -9.14 / 1.72 / 2.43 / 2.43 / 3.88
China / FIO / 17.53 / 29.48 / 9.99 / 21.51 / 1.78 / 2.67 / 3.25 / 5.34
USA / GE3 / 11.74 / -1.27 / -6.93 / 4.81 / 0.41 / 1.48 / 1.95 / 2.54
USA / GF3 / -17.63 / -19.81 / -19.08 / -22.85 / 0.91 / 1.46 / 1.56 / 2.60
Korea / Ha5 / -14.96 / -11.92 / -4.78 / -20.87 / 1.34 / 2.82 / 2.65 / 4.19
France / IP2 / 5.46 / 17.41 / 35.99 / 28.85 / 1.12 / 2.16 / 2.21 / 3.96
Japan / MI2 / -4.90 / 10.38 / 17.87 / 30.61 / 1.75 / 2.29 / 2.39 / 3.72
Japan / MI3 / 16.49 / 12.43 / 28.78 / 57.55 / 2.26 / 3.17 / 3.37 / 4.76
Japan / MI4 / 13.87 / 6.38 / 24.71 / 44.72 / 1.81 / 3.14 / 3.39 / 5.07
Japan / MR3 / -4.52 / 17.67 / -7.23 / 19.86 / 1.15 / 1.54 / 2.01 / 3.19
Norway / NE1 / 5.28 / 12.33 / 21.93 / 18.59 / 1.29 / 2.12 / 2.24 / 3.62
Norway / NE2 / 3.10 / 8.29 / 7.78 / 33.87 / 1.22 / 2.14 / 2.11 / 3.54
Mean / 1.76 / 4.28 / 7.39 / 15.53 / 1.38 / 2.25 / 2.47 / 3.87
Table S3 Economic costs ($ ha-1) of agricultural management practices.
Crop / Sowing / Fertilization / Harvest(for the first 2.5 Mg of grain) / Pest control
Canola / 66 / 56 / 85 / 94
Chickpea / 94 / 47 / 80 / 147
Barley / 66 / 56 / 65 / 64
Sorghum / 46 / 43 / 65 / 84
Wheat / 58 / 56 / 65 / 58
Table S4 Observed and predicted crop biomass and yields (kg ha-1) at harvest, and accumulated N2O (kg N ha-1) emissions for four cropping systems.
T1CaWB / Canola agDMa / 8,725 / 8,584
Canola yield / 3,007 / 2,837
Wheat agDM / 9,663 / 10,671
Wheat yield / 3,092 / 3,098
Barley agDM / 9,823 / 10,465
Barley yield / 4,082 / 4,126
Accumulated N2O / 1.577 / 1.674
T3CpWB / Chickpea agDM / 5,787 / 6,140
Chickpea yield / 1,470 / 1,540
Wheat agDM / 11,499 / 10,659
Wheat yield / 2,935 / 3,094
Barley agDM / 8,334 / 9,429
Barley yield / 3,657 / 3,381
Accumulated N2O / 0.993 / 0.959
T4CpWCp / Chickpea agDM / 5,756 / 6,136
Chickpea yield / 1,533 / 1,540
Wheat agDM / 11,705 / 11,361
Wheat yield / 3,008 / 3,298
Chickpea agDM / 9,060 / 6,260
Chickpea yield / 2,452 / 2,012
Accumulated N2O / 0.571 / 0.734
T5CpS / Chickpea agDM / 7,503 / 7,428
Chickpea yield / 2,270 / 1,862
Sorghum agDM / 20,714 / 19,253
Sorghum yield / 9,773 / 9,316
Accumulated N2O / 1.010 / 0.981
a agDM denotes the aboveground biomass.
Table S5 Means of yield-scaled N2O emissions (g N Mg-1 grain) of four cropping systems for 1952–2014 (reference) and under four RCP scenarios for 2015–2098.
Treatment / Reference / Period / RCP2.6 / RCP4.5 / RCP6.0 / RCP8.5T1CaWB
Canola / 195 / 2015-2056 / 222(±44) A / 210(±65) B / 195(±53) C / 231(±60) A
2057-2098 / 200(±57) D / 257(±79) B / 220(±69) C / 379(±158) A
Wheat / 177 / 2015-2056 / 207(±43) C / 230(±50) B / 190(±52) D / 258(±138) A
2057-2098 / 218(±40) D / 310(±118) B / 273(±100) C / 431(±206) A
Barley / 111 / 2015-2056 / 117(±21) B / 121(±44) B / 118(±29) B / 139(±34) A
2057-2098 / 129(±35) C / 140(±46) B / 137(±32) B / 174(±57) A
T3CpWB
Chickpea / 72 / 2015-2056 / 82(±15) B / 86(±22) B / 79(±23) C / 99(±20) A
2057-2098 / 79(±20) D / 116(±25) C / 128(±38) B / 313(±103) A
Wheat / 110 / 2015-2056 / 134(±23) C / 150(±28) B / 133(±30) C / 168(±71) A
2057-2098 / 137(±28) C / 192(±55) B / 189(±54) B / 258(±109) A
Barley / 100 / 2015-2056 / 106(±24) C / 112(±46) B / 111(±35) B / 127(±33) A
2057-2098 / 119(±33) C / 127(±42) B / 127(±41) B / 159(±72) A
T4CpWCp
Chickpea / 75 / 2015-2056 / 103(±24) C / 123(±34) B / 104(±19) C / 145(±30) A
2057-2098 / 106(±26) D / 176(±50) C / 221(±84) B / 606(±254) A
Wheat / 76 / 2015-2056 / 99(±21) C / 110(±22) B / 94(±24) C / 122(±49) A
2057-2098 / 99(±18) C / 137(±39) B / 135(±51) B / 171(±85) A
Chickpea / 57 / 2015-2056 / 65(±12) D / 74(±21) B / 70(±11) C / 83(±18) A
2057-2098 / 75(±20) D / 84(±14) C / 105(±21) B / 182(±40) A
T5CpS
Chickpea / 63 / 2015-2056 / 61(±9) C / 67(±23) B / 71(±13) C / 74(±13) A
2057-2098 / 52(±16) C / 74(±25) B / 82(±27) B / 167(±59) A
Sorghum / 55 / 2015-2056 / 65(±25) C / 77(±23) B / 63(±24) C / 82(±24) A
2057-2098 / 60(±14) D / 87(±27) C / 103(±46) B / 142(±58) A
Note: different letters presented in the same rows denote a significant difference at P <0.05. The numbers in the parentheses are the standard deviations, indicating the variations among the 15 GCMs.
Fig. S1 Observed soil organic carbon stocks (0–30 cm) in a continuous wheat treatment of no-tillage + stubble retained + 90 kg urea-N ha-1 yr-1 (NTSR90N) at Warwick (QLD) from 1968 to 2011, and predicted soil organic carbon stocks of 0–30 cm in the T1CaWB treatment at Tamworth in NSW from 1965 to 2012. The model simulation began on January 1, 1952.
A) / B)C) / D)
Fig. S2 Observed and predicted A) soil water contents of topsoil, B) soil nitrate contents, C) daily N2O fluxes and D) accumulated N2O emissions of the T1CaWB treatment for 2009–2012.
A)B)
Fig. S3 Observed and predicted A) crop aboveground biomass and B) crop nitrogen concentrations of the T1CaWB treatment for 2009–2012.
A) / B)C) / D)
Fig. S4 Observed and predicted A) soil water contents of topsoil, B) soil nitrate contents, C) daily N2O fluxes and D) accumulated N2O emissions of the T3CpWB treatment for 2009–2012.
A)B)
Fig. S5 Observed and predicted A) crop aboveground biomass and B) crop nitrogen concentrations of the T3CpWB treatment for 2009–2012.