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RESEARCH ARTICLE

Measurements and APSIM modelling of soil C and N dynamics

C. J. Smith https://orcid.org/0000-0002-1087-9093 A F , B. C. T. Macdonald A F , H. Xing A B C , O. T. Denmead A D , E. Wang A , G. McLachlan A , S. Tuomi A , D. Turner D E and D. Chen D
+ Author Affiliations
- Author Affiliations

A CSIRO Agriculture and Food, GPO Box 1700, Canberra, ACT 2601, Australia.

B State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest Agriculture and Forest University, Yangling, Shaanxi 712100, China.

C Current address: NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia.

D School of Agriculture and Food, University of Melbourne, Victoria 3010, Australia.

E Current address: Plant Production and Protection Division, Food and Agriculture Organization of the United Nations, Viale delle Terme di Caracalla, 00153 Rome, Italy.

F Corresponding authors. Email: Chris.J.Smith@csiro.au; Ben.Macdonald@csiro.au

Soil Research 58(1) 41-61 https://doi.org/10.1071/SR19021
Submitted: 6 June 2019  Accepted: 23 September 2019   Published: 16 October 2019

Abstract

Process-based models capture our understanding of key processes that interact to determine productivity and environmental outcomes. Combining measurements and modelling together help assess the consequences of these interactions, identify knowledge gaps and improve understanding of these processes. Here, we present a dataset (collected in a two-month fallow period) and list potential issues related to use of the APSIM model in predicting fluxes of soil water, heat, nitrogen (N) and carbon (C). Within the APSIM framework, two soil water modules (SoilWat and SWIM3) were used to predict soil evaporation and soil moisture content. SWIM3 tended to overestimate soil evaporation immediately after rainfall events, and SoilWat provided better predictions of evaporation. Our results highlight the need for testing the modules using data that includes wetting and drying cycles. Two soil temperature modules were also evaluated. Predictions of soil temperature were better for SoilTemp than the default module. APSIM configured with different combinations of soil water and temperature modules predicted nitrate dynamics well, but poorly predicted ammonium-N dynamics. The predicted ammonium-N pool empties several weeks after fertilisation, which was not observed, indicating that the processes of mineralisation and nitrification in APSIM require improvements. The fluxes of soil respiration and nitrous oxide, measured by chamber and micrometeorological methods, were roughly captured by APSIM. Discrepancies between the fluxes measured with chamber and micrometeorological techniques highlight difficulties in obtaining accurate measurements for evaluating performance of APSIM to predict gaseous fluxes. There was uncertainty associated with soil depth, which contributed to surface emissions. Our results showed that APSIM performance in simulating N2O fluxes should be considered in relation to data precision and uncertainty, especially the soil depths included in simulations. Finally, there was a major disconnection between the predicted N loss from denitrification (N2 + N2O) and that measured using the 15N balance technique.

Additional keywords: ammonium, nitrate, nitrous oxide, soil evaporation, soil moisture, soil respiration, soil temperature.


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