Phase partitioning of ammonium in Australian and European forest soils

Smethurst, P. J.; Matschonat, Gunda; Ballard, Linda; Dingle, Joanne K.

doi:10.1080/00103629909370350

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Results of this study verified the critical role of plant physiological processes in regulating hydrological and biogeochemical processes in forest ecosystems and demonstrated that it is essential to incorporate a dynamic plant growth model in integrated forest ecosystem models. (Smethurst et al, 1999) Note: U(Min, Max) = uniform distribution with the minimum (Min) and maximum (Max) values, i = 1, 2, 3, representing the three soil layers, respectively.…”

Section: Discussionmentioning

confidence: 99%

Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model

et al. 2013

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Global sensitivity analysis is a useful tool to understand process-based ecosystem models by identifying key parameters and processes controlling model predictions. This study reported a comprehensive global sensitivity analysis for DRAINMOD-FOREST, an integrated model for simulating water, carbon (C), and nitrogen (N) cycles and plant growth in lowland forests. The analysis was carried out for multiple long-term model predictions of hydrology, biogeochemistry, and plant growth. Results showed that long-term mean hydrological predictions were highly sensitive to several key plant physiological parameters. Long-term mean annual soil organic C content and mineralization rate were mainly controlled by temperature-related parameters for soil organic matter decomposition. Mean annual forest productivity and N uptake were found to be mainly dependent upon plant production-related parameters, including canopy quantum use efficiency and carbon use efficiency. Mean annual nitrate loss was highly sensitive to parameters controlling both hydrology and plant production, while mean annual dissolved organic nitrogen loss was controlled by parameters associated with its production and physical sorption. Parameters controlling forest production, C allocation, and specific leaf area highly affected long-term mean annual leaf area. Results of this study could help minimize the efforts needed for calibrating DRAINMOD-FOREST. Meanwhile, this study demonstrates the critical role of plants in regulating water, C, and N cycles in forest ecosystems and highlights the necessity of incorporating a dynamic plant growth model for comprehensively simulating hydrological and biogeochemical processes. Figure 4. Results of the two-step global sensitivity analysis for long-term annual mean soil organic matter (SOM) content. (A) The relative sensitivity index (RSI), (B) the first-order sensitivity index (S i ), and (C) the total order index (S TI ) of these identified top 20 parameters through screening procedure. Parameters associated with soil physical properties, plant, and soil carbon and nitrogen cycles are highlighted by grey-filled, blank, and dashed bars, respectively 4396 S. TIAN ET AL.Figure 11. Summary of the first-order sensitivity index (S i ) of E-FAST analysis for each of the nine model outputs. Parameters related to the DON module were excluded from this figure. Parameters associated with soil physical properties, plant, and soil carbon and nitrogen cycles are highlighted by solid, dashed, and dotted rectangles, respectively 4404 S. TIAN ET AL.

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Section: Discussionmentioning

confidence: 99%

Global sensitivity analysis of DRAINMOD-FOREST, an integrated forest ecosystem model

et al. 2013

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“…second harvest). This is probably due to buffering of NH 4 in the soil solution from the pools present in adsorbed form on soil particles (solid phase NH 4 ) (Smethurst et al 1999). It appears that replenishment of nutrients from the solid phase plays an important role in long-term supply of nutrients to the plant.…”

Section: Resultsmentioning

confidence: 99%

“…The solid-liquid partition coefficient (K d ; g L (1 ) for NH 4 -N, P and K was calculated by diluting the soil with 2 mM CaCl 2 (pH 4.8) in soil:solution ratios of 1:2, 1:10, 1:20, 1:40, 1:80 (Smethurst et al 1999). Each treatment was replicated three times.…”

Section: Estimation Of Model Input Parametersmentioning

confidence: 99%

Nutrient uptake of hybrid poplar in competition with weeds using the soil supply and nutrient demand (SSAND) model

Kabba¹,

Knight²,

Rees³

2009

Can. J. Soil. Sci.

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. 2009. Nutrient uptake of hybrid poplar in competition with weeds using the soil supply and nutrient demand (SSAND) model. Can. J. Soil Sci. 89: 197Á207. Mechanistic nutrient uptake models can help gain a quantitative understanding of nutrient uptake by plants under weed-competing conditions. The objectives of this study were to check the applicability of the soil supply and nutrient demand (SSAND) model to predict N, P and K uptake by hybrid poplar (Populus deltoides )Populus)petrowskyana var. Walker) grown with and without competition with dandelion (Taraxacum officinale) and quackgrass (Elymus repens) in a controlled environment, and to determine if incorporating N mineralization into the model would improve N uptake predictions. Simulation results showed that N uptake was underestimated for hybrid poplar by 58 to 73%, depending upon soil type and weed treatment. Incorporation of N mineralization as a model input improved the hybrid poplar N uptake predictions by 24 and 67% in the pasture and alfalfa soil, respectively, when grown without weeds. The SSAND model underestimated P uptake by 84 to 89% and overestimated K uptake by 28 to 59% for hybrid poplar depending upon the soil type and weed treatment. Results from this study illustrate that modeling competition between two species in a controlled environment study is a complex process and that further work is required to understand the underlying processes controlling nutrient supply and uptake in these systems.

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“…Concentrations of NH 4 -N and NO 3 -N in soil solution were determined colorimetrically using the Technicon II autoanalyzer (Technicon Instruments Corp., NY). The solid liquid partition coefficient (K d ) for NH 4 was calculated by diluting the soil with 2 mM CaCl 2 solution in soil:solution ratios of 1:2, 1:10, 1:20, 1:40, and 1:80 (Smethurst et al, 1999). Three replicated samples for each treatment were shaken for 12 h on a rotary shaker, centrifuged and filtered.…”

Section: Soil Parametersmentioning

confidence: 99%