TY - JOUR
T1 - Carbon, water and energy fluxes in agricultural systems of Australia and New Zealand
AU - Cleverly, James
AU - Vote, Camilla
AU - Isaac, Peter
AU - Ewenz, Cacilia
AU - Harahap, Mahrita
AU - Beringer, Jason
AU - Campbell, David I.
AU - Daly, Edoardo
AU - Eamus, Derek
AU - He, Liang
AU - Hunt, John
AU - Grace, Peter
AU - Hutley, Lindsay B.
AU - Laubach, Johannes
AU - McCaskill, Malcolm
AU - Rowlings, David
AU - Rutledge Jonker, Susanna
AU - Schipper, Louis A.
AU - Schroder, Ivan
AU - Teodosio, Bertrand
AU - Yu, Qiang
AU - Ward, Phil R.
AU - Walker, Jeffrey P.
AU - Webb, John A.
AU - Grover, Samantha P.P.
PY - 2020/6/15
Y1 - 2020/6/15
N2 - 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.
AB - 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.
KW - Agriculture
KW - Eddy covariance
KW - Environmental variability
KW - Irrigation
KW - Precipitation pulses
KW - Wavelet-statistics conjunction
UR - http://www.scopus.com/inward/record.url?scp=85079557545&partnerID=8YFLogxK
U2 - 10.1016/j.agrformet.2020.107934
DO - 10.1016/j.agrformet.2020.107934
M3 - Article
AN - SCOPUS:85079557545
SN - 0168-1923
VL - 287
SP - 1
EP - 16
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
M1 - 107934
ER -