Nitrogen mineralisation in sugarcane soils in Queensland, Australia: I. evaluation of soil tests for predicting nitrogen mineralisation

Allen, Diane E.; Bloesch, P. M.; Orton, Thomas G.; Schroeder, B. L.; Skocaj, Danielle; Wang, Weijin; Masters, B.; Moody, Phil

doi:10.1071/sr19031

Cited by 10 publications

(10 citation statements)

References 55 publications

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“…However, N min involves complex biochemical processes and its measurement requires time‐consuming incubations (Ameloot et al, 2015; Bushong et al, 2007). Therefore, simple indicators such as predictors of potential N min rate are important (Allen et al, 2019; Bushong et al, 2007; Martínez et al, 2017). The significant correlation between potential N min rate and HWEC (Figure 4) may suggest a potential possibility to use HWEC as an indicator for potential N min rate.…”

Section: Discussionmentioning

confidence: 99%

Effect of grazing exclusion and rotational grazing on labile soil organic carbon in north China

Dong

Martinsen

et al. 2020

European J Soil Science

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Overgrazing has caused serious grassland soil deterioration and significant soil organic carbon (SOC) losses. Controlled grazing is recommended for restoring grassland and enhancing SOC stocks. Studies have suggested that, in particular, labile organic carbon (C) is a sensitive indicator for soil degradation and SOC recovery, but few studies of the effect of different grassland management practices have been published so far. Here, we investigate the effects of long‐term grazing exclusion and short‐term rotational grazing with different grazing intensities on labile SOC, including hot water extractable organic C (HWEC) and particulate organic C (POC), as well as potential N mineralization (Nmin) rate in soils (0–10 cm) of two grasslands in Xilinhot, Inner Mongolia. Treatments include long‐term (34 years) exclusion, medium‐term (21 years) exclusion and continuous grazing (ambient grazing) at one of the sites, and we investigate no grazing, light grazing, moderate grazing and high‐grazing intensity at the study site for rotational grazing. Our results show that grazing exclusion significantly enhances labile SOC stocks and potential Nmin rate compared to ambient grazing (e.g., POC stock at long‐ and medium‐term exclusion and ambient grazing are 5.71 ± 0.94 Mg C ha−1, 3.25 ± 0.30 Mg C ha−1 and 2.12 ± 0.38 Mg C ha−1, respectively). However, after two decades of exclusion, the potential for further C sequestration is limited. Five years of rotational grazing significantly enhances HWEC, but not POC and potential Nmin rate, when comparing the highest grazing intensity (1.88 sheep ha−1 yr−1) with grazing exclusion during the same period. We conclude that rotational grazing may be a viable alternative to exclusion, offering a more rational use of grassland without causing soil C loss. We also show that HWEC is a particularly sensitive labile SOC indicator and is tightly correlated to POC and potential Nmin rate. Highlights We investigated effects of grazing exclusion and rotational grazing on labile soil organic carbon. Grazing exclusion increased labile SOC and the highest level appeared in medium‐term exclusion. Five years of high‐intensity rotational grazing increased HWEC with no significant effects on POC. Rotational grazing is a viable alternative to grazing exclusion management.

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Section: Discussionmentioning

confidence: 99%

Effect of grazing exclusion and rotational grazing on labile soil organic carbon in north China

Dong

Martinsen

et al. 2020

European J Soil Science

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“…Even though a soil test may not reflect in-field nitrate levels, it may still indicate overall N availability. This suggests that formalised assessment of mineralisable N, as has been expanding in other cropping industries such as sugarcane, may have greater value for predicting fertiliser requirements (Allen et al 2019;Brackin et al 2019). As with research applications, details of soil sample handling and storage, such as time and temperature, should be considered when interpreting commercial soil test results.…”

Section: Discussionmentioning

confidence: 99%

“…However, inconsistency in sampling methods and data interpretation makes test results less reliable for growers (Schwenke et al 2019). In research trials comparing N fertiliser forms, application rates, management techniques or loss prevention strategies, gaining accurate measurements of nitrate, ammonium and DON is critical to evaluating plant N availability, susceptibility to loss and ultimately NUE (Shaw et al 2014;Rochester and Bange 2016;Macdonald et al 2017a;Allen et al 2019). Ensuring routine sampling and analysis procedures give accurate representations of N form distribution is crucial to both developing effective strategies for improving NUE and informing improved fertiliser management.…”

Section: Introductionmentioning

confidence: 99%

Storage of soil samples leads to over-representation of the contribution of nitrate to plant-available nitrogen

et al. 2021

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Delays between soil sampling and processing for analysis are common in both research and agronomy, but the effects of storage conditions on measurements of plant-available nitrogen (N) are rarely considered. With increasing recognition of organic N pools in soils, such as amino acids and peptides, it is necessary to determine how sample handling impacts the outcomes of soil N quantification. In this study, we used in situ microdialysis to approximate plant availability of amino acids, ammonium and nitrate, then compared to both potassium chloride (KCl) extract and microdialysis samples taken from excavated soil samples when in the field, after 24h refrigerated storage, and after storage for 1month, either refrigerated or air-dried. Nitrate levels measured with microdialysis and KCl extracts increased immediately after soil sampling and continued to accumulate in the next day and 1month stored samples. Amino acid and ammonium measurements remained more constant; however, microdialysis showed a decline in amino acid-N between in situ and next day samples. The proportional representation of N pools in the in-field extracts was most similar to in situ microdialysis.Soil samples should be processed for N analysis as close to sampling as possible, and the storage duration and conditions reported. The influence of storage must be considered in interpreting soil test results.

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“…We used the Agricultural Production Systems Simulator (APSIM; http://www.apsim. info/; Holzworth et al, 2014) to simulate a nitrogen mineralization experiment undertaken by Allen et al (2019). Calibration of APSIM to the data from Allen et al (2019) was used to estimate two parameters in the APSIM model: "finert", which represents the proportion of initial organic carbon assumed to be inert in the soil, and "fbiom", which represents the proportion of non-inert carbon in the microbial biomass within the soil.…”

Section: Introductionmentioning

confidence: 99%

“…info/; Holzworth et al, 2014) to simulate a nitrogen mineralization experiment undertaken by Allen et al (2019). Calibration of APSIM to the data from Allen et al (2019) was used to estimate two parameters in the APSIM model: "finert", which represents the proportion of initial organic carbon assumed to be inert in the soil, and "fbiom", which represents the proportion of non-inert carbon in the microbial biomass within the soil. These two parameters are used in APSIM to calculate the initial size of two conceptual carbon pools: biom (microbial biomass) and hum (the rest of soil organic matter), where mineralization of organic carbon occurs (Probert et al, 1998(Probert et al, , 2005.…”

Section: Introductionmentioning

confidence: 99%

Considering unknown uncertainty in imperfect models: nitrogen mineralization as a case study

2021

MODSIM2021, 24th International Congress on Modelling and Simulation.

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All models are imperfect, so it is important to consider uncertainty in their predictions. When calibrating models to measured data, uncertainty in the model can be simultaneously estimated, although there are multiple methods available for doing this. In this study, we applied ensemble smoothers and Bayesian inference to calibrate a model of nitrogen mineralization in soils. We obtained mineralization measurements from a previously published study that measured changes in inorganic nitrogen over long-term laboratory incubations in a soil located in the Mackay Whitsundays region (North Queensland). Simulations were performed using the Agricultural Production Systems Simulator (APSIM). We inferred two parameters that characterize the size of the simulated soil organic carbon pools (fbiom and finert) because it is difficult to estimate these parameters from measurements only. For the calibration, we considered two different sources of uncertainty: measurement noise and noise of unknown origin, the latter of which includes all nonmeasurement related errors. We found that ignoring noise of unknown origin can result in an overly optimistic representation of the model error (Figure 1a,c). On the contrary, incorporating noise of unknown origin can lead to a more accurate representation of the uncertainty in the predictions, with model predictions providing adequate coverage of measurements (Figure 1b,d). We show that parameterizing fbiom and finert is difficult because these parameters are correlated, hence different combinations of parameters can equally well simulate the measured data. We suggest that future work needs to provide a means of parameterizing at least one of these fractions independently to facilitate parameter identifiability. Figure 1. Measured (grey dots) and simulated (orange lines) nitrogen mineralization (mg N kg -1 ) for the ensemble smoother with multiple data assimilation (ES-MDA) (a), flexible iterative ES-MDA (b), Bayesian inference with measurement noise only (c), Bayesian inference with measurement noise and additional noise (d). The error bars in the measured data represent the error of the laboratory method. Shaded areas represent the predicted 95% credible intervals.

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Nitrogen mineralisation in sugarcane soils in Queensland, Australia: I. evaluation of soil tests for predicting nitrogen mineralisation

Cited by 10 publications

References 55 publications

Effect of grazing exclusion and rotational grazing on labile soil organic carbon in north China

Effect of grazing exclusion and rotational grazing on labile soil organic carbon in north China

Storage of soil samples leads to over-representation of the contribution of nitrate to plant-available nitrogen

Considering unknown uncertainty in imperfect models: nitrogen mineralization as a case study

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