4.7 Article

Assessment of orchard N losses to groundwater with a vadose zone monitoring network

期刊

AGRICULTURAL WATER MANAGEMENT
卷 172, 期 -, 页码 83-95

出版社

ELSEVIER SCIENCE BV
DOI: 10.1016/j.agwat.2016.04.012

关键词

Nitrate; Best-management-practice; Leaching; Fertigation; Flooding

资金

  1. California Department of Food and Agriculture's Fertilizer Research and Education Program (CDFA-FREP) [12-0454-SA]
  2. Almond Board of California [13PREC6SMART]
  3. California Pistachio Research Board [2013-02890]
  4. Belgian American Educational Foundation (BAEF)
  5. Wallonie-Bruxelles International (WBI) with a WBI.WORLD excellence grant
  6. Fonds Speciaux de Recherche (FSR) of Universite catholique de Louvain

向作者/读者索取更多资源

A 2-year study was conducted to explore the impact of current and alternative best management practices (BMPs) of irrigation and fertigation on nitrate (NO3-) leaching below the root zone. Using a fully randomized complete block design, three fertigation strategies were compared: current BMP with and without accounting for NO3--N in irrigation-water, and a high frequency fertigation treatment with low N concentration applications. 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 in an almond and a pistachio orchard. NO3- concentrations below the root zone ranged from <1 mg L-1 to more than 2400 mg L-1 (almond), and up to 11,000 (pistachio) mg L-1, with mean concentrations of 326 and 4631 mg L-1, respectively. Within the fertigation cycle, fertilizer injection at the end of an irrigation event generally resulted in lower NO3- losses below the root zone compared with fertilizer injection midway through the irrigation. Pre-bloom and post-harvest flood irrigation in the almond orchard caused deep soil wetting and flushing of NO3- below the root zone, threatening groundwater quality. Statistical analysis using principal component analysis, Chi-squared Automatic Interaction Detector and the Artificial Neural Network showed that most of the deep soil NO3- concentration variability could not be explained by irrigation duration, fertigation timing or local variations in soil physical characteristics. However, mass balance estimates for water and N indicated the annual orchard average N loss could be estimated based on eight monitoring sites in spite of the inherent spatial variations in soil properties and the spatiotemporal variations in water and NO3- applications. The study indicated that reduction of N losses at the orchard scale would require alternative fertigation and irrigation practices, including better control of fertigation amounts and irrigation duration. (C) 2016 Elsevier B.V. All rights reserved.

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