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Sonya Colour Collection fue creada para ayudar que usted tenga una apariencia tan bella por fuera como saludable por dentro. Ingredientes vitales naturales en

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Table 3.1: Original and updated green and brown leaf reflectivity and transmissivity for PAR and NIR wavelengths.

New PAR Old PAR New NIR Old NIR

Green Reflectivity .08a .10 .38b .58

Brown Reflectivity .36a .36 .53b .58

Green Transmissivity .06a .07 .37b .25

Brown Transmissivity .08a .22 .21b .38

aFrom integrating sphere data for miscanthus leaves in this study

bFrom hyperspectral measurements of Asner et al. 1998

3 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Table 3.2: Summary of climate-smart scenarios4

Description Years Simulated

Maize (MZ) Default Agro-IBIS maize parameterization 1979-1998; 2049-2068 Maize-short

(MZ_1260)

Planting date allowed to respond to yearly meteorology, GDDmata set to 1260

2049-2068 Maize-long

(MZ_1610)

Planting date allowed to respond to yearly meteorology, GDDmata set to 1610

2049-2068 Maize-no

till (MZ_nt)

Default Agro-IBIS maize with top soil layer representing maize debrisb

1979-1998; 2049-2068 Perennial

Crop (PR)

Represented by Miscanthus cultivar as described in Vanloocke et al. (2012)

1979-1998; 2049-2068

aGDDmat represents the # of growing degree days a crop needs to reach maturity from planting. This parameter was set based on data from Sacks et al., 2011

bUsing parameters derived in Kucharik et al., 2012

4 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Table 3.3: Albedo comparison between Perennial Grass (PR) and Maize (MZ) scenarios for winter (DJF) and summer (JJA) at Champaign, IL. Winter albedo is separated by days with snow and days without. Snow days are considered to be when the fractional area snow area for a grid cell is greater than 0.1.5

DJF 1979-1998 DJF 2049-2068 JJA

Overall Snow No-

Snow Overall Snow

No- Snow 1979- 1998 2049- 2068 Perennial Grass .320 .408 .238 .264 .387 .232 .177 .180 Maize .359 .519 .186 .248 .462 .169 .176 .178

5 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.1: Observed (dashed) and simulated (solid line) 14-day running mean canopy albedo (unitless) for maize (black) and miscanthus (red) for 2010-2012 at Champaign, IL. Blue line is miscanthus simulation with original Agro-IBIS parameters.6

6 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.2: DJF (a, b, e) and JJA (c, d) mean difference in surface temperature (K; a, c),

precipitation (mm day-1; b, d), and simulated fractional snow cover (unitless; e) between 2049- 2068 and 1979-1998.7

7 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.3: Mean change in maize emergence date (a) and maturity date (b) of future vs. current climate for MZ_1610 scenario. The zonal mean of the map is shown on the right of each plot.8

8 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.4: 2049-2068 maize LAI for 1260 GDD (dashed line) vs. 1610 GDD cultivar (solid line) at Champaign, IL. The period from day ~240-250 when the MZ_1260 scenario has an LAI near 1 while MZ_1610 is an artifact of Agro-IBIS’ crop phenology routine for this location, and is not considered in this analysis.9

9 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.5: 2049-2068 mean Rn (Wm-2; a), H (Wm-2, b), L (Wm-2, c), G (Wm-2, d), and soil

temperature of top 5cm of soil (K; e) for the MZ_1610 scenario (red line) and MZ_1260 scenario (black line) over the period when difference in LAI between MZ_1260 and MZ_1610 were greater than 0.1.10

10 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.6: 1979-1998 (a) Impact of no-till on annual mean fractional volumetric water content (unitless) over top 1-m of soil as indicated by the difference between the MZ and MZ_notill scenarios, and (b) the difference between the MZ and MZ_notill scenarios for annually reflected solar radiation (MJ m-2 year-1). Similar patterns of moisture retention are exhibited throughout the deeper layers of the soil column as well (not shown). The hatching represents statistical significance of .95 using a paired t-test.11

11 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.7: Difference in 1979-1998 mean seasonal albedo (unitless) between MZ and MZ_notill for DJF (a), MAM (c), JJA (b), SON (d), and 2049-2068 DJF (e). The hatching represents statistical significance of .95 using a paired t-test.12

12 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.8: The 1979-1998 mean differences in Rn (W m-2) for MAM and SON for the MZ_nt-MZ

scenarios are shown in (a) and (d). (b) and (e) show the 1979-1998 MAM and SON changes in latent heat flux as a percentage of the change in Rn given in (a) and (d) respectively. Similarly, (c) and (f) show the 1979-1998 MAM and SON changes in sensible heat flux as a percentage of the change in Rn given in (a) and (d).13

13 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.9: The bottom portion of the figure gives the mean leaf area index (m2 m-2) for MZ (red lines) and PR (black lines) at Champaign, IL from 1979-1998. The top portion of the figure shows the corresponding cumulative reflected solar radiation at Champaign, IL (MJ m-2 year-1) averaged over 1979-1998 (solid lines) and 2049-2068 (dashed lines).14

14 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.10: Difference in SON (a,b) and DJF (c,d) mean net solar radiation (W m-2) between the perennial and maize scenarios (PR-MZ) for 1979-1998 (a,c) and 2049-2068 (b,d). Additionally, the zonal mean of the map is shown on the right of each plot. The hatching represents statistical significance of .95 using a paired t-test.15

15 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.11: The difference in mean annual evapotranspiration of PR-MZ (mm year-1) for 1979- 1998 (a). The difference in seasonal latent heat flux (W m-2) is shown for DJF (b), MAM (d), JJA (c), and SON (e). The hatching represents statistical significance of .95 using a paired t- test.16

16 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.12: 1979-1998 difference in PR vs. MZ scenarios for H (W m-2). The hatching represents statistical significance of .95 using a paired t-test.17

17 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.13: Difference in 1979-1998 mean yearly net solar radiation (W m-2) between MZ_1610 and MZ_1260. The hatching represents statistical significance of .95 using a paired t-test.18

18 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

Fig. 3.14: Simulated 2049-2068 differences between autumn and spring (SON-MAM) volumetric water content (unitless) for top 1-m of soil.19

19 The work in this chapter has been previously published.

Bagley, J. E., Miller, J., & Bernacchi, C. J. (2015). Biophysical impacts of climate-smart agriculture in the Midwest United States. Plant, Cell & Environment, 38(9), 1913–1930. http://doi.org/10.1111/pce.12485

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