Nutrient uptake estimates for woody species as described by the NST 3.0, SSAND, and PCATS mechanistic nutrient uptake models

Abstract

With the advent of the personal computer, mechanistic nutrient uptake models have become widely used as research and teaching tools in plant and soil science. Three models NST 3.0, SSAND, and PCATS have evolved to represent the current state of the art. There are two major categories of mechanistic models, transient state models with numerical solutions and steady state models. NST 3.0 belongs to the former model type, while SSAND and PCATS belong to the latter. NST 3.0 has been used extensively in crop research but has not been used with woody species. Only a few studies using SSAND and PCATS are available. To better understand the similarities and differences of these three models, it would be useful to compare model predictions with experimental observations using multiple datasets from the literature to represent various situations for woody species. Therefore, the objectives of this study are to: (i) compare the predictions of uptake by the NST 3.0, SSAND, and PCATS models for a suite of nutrients against experimentally measured values, (ii) compare the behavior of the three models using a one dimensional sensitivity analysis; and (iii) compare and contrast the behavior of NST 3.0 and SSAND using a multiple dimensional sensitivity analysis approach. Predictions of nutrient uptake by the three models when run with a common data set were diverse, indicating a need for a reexamination of model structure. The failure of many of the predictions to match observations indicates the need for further studies which produce representative datasets so that the predictive accuracy of each model can be evaluated. Both types of sensitivity analyses suggest that the effect of soil moisture on simulation can be influential when nutrient concentration in the soil solution (C Li ) is low. One dimensional sensitivity analysis also revealed that I max negatively influenced the uptake estimates from the SSAND and PCATS models.Further analysis indicates that this counter intuitive response of I max is probably related to low soil nutrient supply. The predictions of SSAND under low-nutrient-supply scenarios are generally lower than those of NST 3.0. We suspect that both of these results are artifacts of the steady state models and further studies to improve them, such as incorporating important rhizospheric effects, are needed if they are to be used successfully for the longer growth periods and lower soil nutrient supply situations more typical of woody species.iii Acknowledgements

Diel plant water use and competitive soil cation exchange interact to enhance NH4 + and K+ availability in the rhizosphere

“…Despite this need to understand coupled hydrologic and biogeochemical interactions in the rhizosphere, models often use simplified representations of hydrologic and geochemical processes. Instead of simulating diel plant water use (i.e., transpiration during the day and no water use during the night), a steady root-ward water flux is frequently imposed; instead of simulating competitive cation exchange, sorption interactions between dissolved nutrients and soil are often represented with a linear sorption isotherm or buffer coefficient, which implies that partitioning of a given ion between the aqueous and solid phase is independent of and unaffected by concentrations and partitioning behavior of other ions (e.g., Claassen et al 1986;Barber 1995;Tinker and Nye 2000;Nowack et al 2006;Lin and Kelly 2010). We hypothesized that these two often-neglected processes -diel plant water use and competitive cation exchange -could interact to alter nutrient availability and nutrient concentration patterns in the rhizosphere, with implications for understanding rhizosphere nutrient cycling and plant nutrient uptake.…”

Acquisition of phosphorus and other poorly mobile nutrients by roots. Where do plant nutrition models fail?

“…Nutrient uptake models developed in forest ecosystems since the 1960s have shown similar evolutions, but no revolutions, over the last decades (Kelly and Ericsson 2003;Smethurst and Comerford 1993;Williams and Yanai 1996). Recent versions started to take into account the effects of fertilizer inputs and nutrient uptake by mycorrhizae Lin and Kelly 2010). However, a comparison of nutrient uptake predictions against experimentally measured values showed that the last version of three process-based models (NST 3.0, SSAND, and PCATS) largely underestimated P uptake for three woody plant species, except under large P fertilizer additions for the transient state model NST 3.0 developped by Classen and co-workers (e.g.…”

“…This pattern showed that including mycorrhizal uptake in the simulations was not sufficient to predict accurately nutrient uptake under the low nutrient concentrations typically occurring in forest soils. This study suggested that rhizospheric effects, not yet taken into account in these models, should be implemented to improve their predictive ability (Lin and Kelly 2010).…”

Modeling Phosphorus Capture by Plants Growing in a Multispecies Riparian Buffer

“…Although results indicate a pure stand of cottonwood would be more effective in capturing solution P, it should be kept in mind that the grass communities will be more effective in mitigating or preventing the loss of particulate P due to soil erosion. While the results of this study are most encouraging, recent evaluations of the current approaches to mechanistic modeling [10,15,32] point to the need for a further evolution of the structure of minimalistic mechanistic nutrient uptake models. In the final analysis, it is important to remind ourselves that the validity of predictions produced by this or any model, is highly dependent on the quality of the data used to represent each of the parameters.…”

“…NST 3.0 provides a means to simulate the impacts of changes in both plant and soil processes on the uptake of P for a variety of plant species. In a recent study Lin and Kelly [15] found that NST 3.0 provided the best estimates of P uptake in a three-model comparison utilizing a common set of input values.…”