Carbon, water and energy fluxes in agricultural systems of Australia and New Zealand
Cleverly, J
Vote, C
Isaac, P
Ewenz, C
Harahap, M
Beringer, J
Campbell, DI
Daly, E
Eamus, D
He, L
Hunt, J
Grace, P
Hutley, LB
Laubach, J
McCaskill, M
Rowlings, D
Rutledge Jonker, S
Schipper, LA
Schroder, I
Teodosio, B
Yu, Q
Ward, PR
Walker, JP
Webb, JA
Grover, SPP
Vote, C
Isaac, P
Ewenz, C
Harahap, M
Beringer, J
Campbell, DI
Daly, E
Eamus, D
He, L
Hunt, J
Grace, P
Hutley, LB
Laubach, J
McCaskill, M
Rowlings, D
Rutledge Jonker, S
Schipper, LA
Schroder, I
Teodosio, B
Yu, Q
Ward, PR
Walker, JP
Webb, JA
Grover, SPP
- Publisher:
- Elsevier BV
- Publication Type:
- Journal Article
- Citation:
- Agricultural and Forest Meteorology, 2020, 287 pp. 107934 - 107934
- Issue Date:
- 2020-06-15
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A comprehensive understanding of the effects of agricultural management on climate–crop interactions has yet to emerge. Using a novel wavelet–statistics conjunction approach, we analysed the synchronisation amongst fluxes (net ecosystem exchange NEE, evapotranspiration and sensible heat flux) and seven environmental factors (e.g., air temperature, soil water content) on 19 farm sites across Australia and New Zealand. Irrigation and fertilisation practices improved positive coupling between net ecosystem productivity (NEP = −NEE) and evapotranspiration, as hypothesised. Highly intense management tended to protect against heat stress, especially for irrigated crops in dry climates. By contrast, stress avoidance in the vegetation of tropical and hot desert climates was identified by reverse coupling between NEP and sensible heat flux (i.e., increases in NEP were synchronised with decreases in sensible heat flux). Some environmental factors were found to be under management control, whereas others were fixed as constraints at a given location. Irrigated crops in dry climates (e.g., maize, almonds) showed high predictability of fluxes given only knowledge of fluctuations in climate (R2 > 0.78), and fluxes were nearly as predictable across strongly energy- or water-limited environments (0.60 < R2 < 0.89). However, wavelet regression of environmental conditions on fluxes showed much smaller predictability in response to precipitation pulses (0.15 < R2 < 0.55), where mowing or grazing affected crop phenology (0.28 < R2 < 0.59), and where water and energy limitations were balanced (0.7 < net radiation ∕ precipitation < 1.3; 0.27 < R2 < 0.36). By incorporating a temporal component to regression, wavelet–statistics conjunction provides an important step forward for understanding direct ecosystem responses to environmental change, for modelling that understanding, and for quantifying nonstationary, nonlinear processes such as precipitation pulses, which have previously defied quantitative analysis.
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