About Us | Help Videos | Contact Us | Subscriptions
 

Members of ASA, CSSA, and SSSA: Due to system upgrades, your subscriptions in the digital library will be unavailable from May 15th to May 22nd. We apologize for any inconvenience this may cause, and thank you for your patience. If you have any questions, please call our membership department at 608-273-8080.

 

Institutional Subscribers: Institutional subscription access will not be interrupted for existing subscribers who have access via IP authentication, though new subscriptions or changes will not be available during the upgrade period. For questions, please email us at: queries@dl.sciencesocieties.org or call Danielle Lynch: 608-268-4976.

Abstract

 

This article in JEQ

  1. Vol. 39 No. 5, p. 1699-1710
    unlockOPEN ACCESS
     
    Received: Sept 3, 2009


    * Corresponding author(s): david.nash@dpi.vic.gov.au
 View
 Download
 Alerts
 Permissions
Request Permissions
 Share

doi:10.2134/jeq2009.0348

A Bayesian Network for Comparing Dissolved Nitrogen Exports from High Rainfall Cropping in Southeastern Australia

  1. David Nash *a,
  2. Murray Hannaha,
  3. Fiona Robertsonb and
  4. Penny Rifkinb
  1. a Victorian Dep. of Primary Industries–Ellinbank, RMB 2460 Hazeldean Road, Ellinbank, Victoria 3821, Australia
    b Victorian Dep. of Primary Industries–Hamilton, Private Bag 105 Hamilton VIC 3300, Australia. Assigned to Associate Editor Robert Hubbard

Abstract

Best management practices are often used to mitigate nutrient exports from agricultural systems. The eff ectiveness of these measures can vary depending on the natural attributes of the land in question (e.g., soil type, slope, and drainage class). In this paper we use a Bayesian Network to combine experiential data (expert opinion) and experimental data to compare farmscale management for different high-rainfall cropping farms in the Hamilton region of southern Australia. In the absence of appropriate data for calibration, the network was tested against various scenarios in a predictive and in a diagnostic way. In general, the network suggests that transport factors related to total surface water (i.e., surface and near surface interfl ow) runoff, which are largely unrelated to Site Variables, have the biggest effect on N exports. Source factors, especially those related to fertilizer applications at planting, also appear to be important. However, the effects of fertilizer depend on when runoff occurs, and, of the major factors under management control, only the Fertilizer Rate at Sowing had a notable effect. When used in a predictive capacity, the network suggests that, compared with other scenarios, high N loads are likely when fertilizer applications at sowing and runoff coincide. In this paper we have used a Bayesian Network to describe many of the dependencies between some of the major factors affecting N exports from high rainfall cropping. This relatively simple approach has been shown to be a useful tool for comparing management practices in data-poor environments.

  Please view the pdf by using the Full Text (PDF) link under 'View' to the left.

Copyright © 2010. American Society of Agronomy, Crop Science Society of America, Soil Science SocietyAmerican Society of Agronomy, Crop Science Society of America, and Soil Science Society of America