SIMULATING NO3-N TRANSPORT TO SUBSURFACE DRAIN FLOWS AS AFFECTED BY TILLAGE UNDER CONTINUOUS CORN USING MODIFIED RZWQM

Authors
Citation
P. Singh et Rs. Kanwar, SIMULATING NO3-N TRANSPORT TO SUBSURFACE DRAIN FLOWS AS AFFECTED BY TILLAGE UNDER CONTINUOUS CORN USING MODIFIED RZWQM, Transactions of the ASAE, 38(2), 1995, pp. 499-506
Citations number
21
Categorie Soggetti
Engineering,Agriculture,"Agriculture Soil Science
Journal title
ISSN journal
00012351
Volume
38
Issue
2
Year of publication
1995
Pages
499 - 506
Database
ISI
SICI code
0001-2351(1995)38:2<499:SNTTSD>2.0.ZU;2-G
Abstract
The Root Zone Water Quality Model (RZWQM) was previously modified to s imulate subsurface drain flows and evaluate the impact of different ti llage systems on subsurface drain flows (Singh and Kanwar, 1994). This article discusses further modifications made in the RZWQM to simulate nitrate-nitrogen (NO3-N) concentrations and NO3-N losses with subsurf ace drain flows. Daily NO3-N concentrations were simulated in subsurfa ce drain flows under four different tillage systems: chisel plow (CP), moldboard plow (MB), no-tillage (NT), and ridge-tillage (RT) by using the modified RZWQM. Simulations were conducted for the growing season s of three years (1990 to 1992). Simulated NO3-N concentrations and lo sses with subsurface drain flows were compared with the measured data obtained from a water quality research site at Nashua, Iowa. Predicted NO3-N concentrations generally followed the same pattern as the obser ved concentrations. Simulated annual average NO3-N concentrations in s ubsurface drain flows were within 11% (averaged over all three years) of observed annual average NO3-N concentrations in subsurface drain fl ows. The model correctly predicted maximum concentrations under MB tre atment and minimum under NT for all three years. Simulated annual NO3- N losses were within 14% (averaged over all three years) of observed a nnual NO3-N losses. Various NO3-N transformation processes need to be calibrated as a function of tillage system to improve model performanc e.