Estimating nitrate leaching to groundwater from orchards: Comparing crop Nitrogen excess, deep vadose zone data-driven estimates, and HYDRUS modeling
(2016) Vadose Zone Journal — Vol. 15, n° 11, p. 13 p. (2016)
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- Large spatial and temporal variability in water flow and N transport dynamics poses significant challenges to accurately estimating N losses form orchards. A 2-yr study was conducted to explore nitrate (NO3−) leaching below the root zone of an almond [Prunus dulcis (Mill.) D. A. Webb] orchard. Temporal changes in water content, pore water NO3− concentrations and soil water potential were monitored within and below the root zone to a soil depth of 3 m at eight sites, which represented spatial variations in soil profiles within an almond orchard in California. Orchard monthly average NO3− concentrations below the root zone ranged from 225 to 710 mg L−1 with mean annual concentration of 468 and 333 mg L−1 for the 2014 and 2015 growing seasons, respectively. Despite the huge variability in pore water NO3− concentration between sites, the larger spatiotemporal scale N losses estimated at the annual orchard scale from surface N mass balance, vadose zone based water and N mass balance, flow calculations, and HYDRUS modeling were all on the same order of magnitude (80-240 kg N ha−1 y−1). All methods indicated that most of the N losses occur early in the growing season (February-May) when fertilizer is applied to wet soil profiles. Simple mass balance (i.e., N load applied minus N load removed) provided a good proxy of the annual N accumulation in the soil profile at the orchard scale. Reduction of N losses at the orchard scale would require alternative fertigation and irrigation practices to decrease the difference between the N load removed and the N load applied to orchards. © Soil Science Society of America.
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Baram, S., Couvreur, V., Read, M., Harter, T., Brown, P. H., Kandelous, M., Smart, D. R., & Hopmans, J. W. (2016). Estimating nitrate leaching to groundwater from orchards: Comparing crop Nitrogen excess, deep vadose zone data-driven estimates, and HYDRUS modeling. Vadose Zone Journal, 15(11), 13 p. https://doi.org/10.2136/vzj2016.07.0061 (Original work published 2016)