|Anderson, Raymond - Ray|
|Kustas, William - Bill|
Submitted to: Agricultural Water Management
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/31/2016
Publication Date: 8/8/2016
Publication URL: http://handle.nal.usda.gov/10113/5598194
Citation: Anderson, R.G., Alfieri, J.G., Tirado-Corbala, R., Gartung, J.L., McKee, L.G., Prueger, J.H., Wang, D., Ayars, J.E., Kustas, W.P. 2016. Assessing FAO-56 dual crop coefficients using eddy covariance flux partitioning. Agricultural Water Management. 179:92-102. doi: 10.1016/j.agwat.2016.07.027.
Interpretive Summary: Agricultural crop water consumption models are important for farmers and irrigation managers to schedule irrigation to maximize crop water use. They are also used by land and resource managers to parameterize hydrologic models to predict climatic and management impacts on water resources. One of the most commonly used models, which is recommended by the Food and Agricultural Organization, estimates water consumption by combining a reference evaporative demand equation with a multiple coefficient approach that separately represent plant transpiration, plant water stress, and soil evaporation. These coefficients better represent physical processes controlling water use, but they are difficult to validate because the most commonly used techniques to measure agricultural water consumption measure the combined plant transpiration and soil evaporation. In this study, we apply a variance technique to Eddy Covariance (a commonly used approach for measuring crop water use) to separate out plant transpiration and soil evaporation. We then use these separated components to directly estimate plant transpiration, water stress, and evaporation coefficients. We illustrate how these measured coefficients can be combined with satellite and soil moisture data to better schedule irrigation. The results benefit irrigation managers, farmers, and hydrologists who rely on crop coefficients to accurately predict water demands, with a particular benefit to farmers and irrigators exploring new production systems to improve production and efficiency.
Technical Abstract: Current approaches to scheduling crop irrigation using reference evapotranspiration (ET0) recommend using a dual-coefficient approach using basal (Kcb) and soil (Ke) coefficients along with a stress coefficient (Ks) to model crop evapotranspiration (ETc), [e.g. ETc=(Ks*Kcb+Ke)*ET0]. However, determining Ks, Kcb, and Ke from the combined evapotranspiration (ET) is challenging, particularly for Ke, and a new method is needed to more rapidly determine crop coefficients for novel cultivars and cultivation practices. In this study, we partition eddy covariance ET observations into evaporation (E) and transpiration (T) components using correlation structure analysis of high frequency (10-20 Hz.) observations of carbon dioxide and water vapor (Scanlon and Sahu, 2008) at three irrigated agricultural sites. These include a C4 photosynthetic-pathway species (sugarcane – Sacharum officinarum L.) and a C3 pathway species (peach - Prunus persica) under sub-surface drip and furrow irrigation, respectively. Both sites showed high overall Kc consistent with their height (>4 m). The results showed differences in Ke, with the sub-surface drip-irrigated sugarcane having a low Ke (0.1). There was no significant relationship (r2<0.05) between root zone soil volumetric water content (VWC) in sugarcane and observed Kcb*Ks, indicating that there was no stress (Ks=1), while the peach orchard showed mid-season declines in Kcb*Ks when VWC declined below 0.2. Partitioning of Kc into Kcb and Ke resulted in a better regression (r2=0.43) between the Normalized Differential Vegetation Index (NDVI) and Kcb in sugarcane than between NDVI and Kc (r2=0.11). The results indicate the potential for correlation structure flux partitioning to improve crop ET coefficient determination by improved use of eddy covariance observations compared to traditional approaches of lysimeters and microlysimeters and sap flow observations to determine Kc, Ke, Ks, and Kcb.