Short-term carbon and nitrogen dynamics in soil,litterfall and canopy of a suburban native forest subjected to prescribed burning in subtropical Australia

Purpose

This study aimed to understand the mechanisms of the variations in carbon (C) and nitrogen (N) pools and examine the possibility of differentiating the burning effects from seasonal and pre-existed N limitations in a native suburban forest ecosystem influenced by prescribed burning in subtropical Australia.

Materials and methods

Soil and litterfall samples were collected from two study sites from 1 to 23 months since last burnt. Soil labile C and N pools, soil C and N isotopic compositions (δ¹³C and δ¹⁵N), litterfall mass production (LM), and litterfall total C, total N, δ¹³C and δ¹⁵N were analysed. In-situ gas exchange measurements were also conducted during dry and wet seasons for Eucalyptus baileyana and E. planchoniana.

Results and discussion

The results indicated that labile C and N pools increased within the first few months after burning, with no correlations with climatic factors. Therefore, it was possible that the increase was due to the burning-induced factors such as the incorporation of ashes into the soil. The highest values of soil and litterfall δ¹⁵N, observed when the study was commenced at the experimental sites, and their high correlations with climatic factors were indicative of long-term N and water limitation. The ¹³C signals showed that soil N concentrations and climatic factors were also two of the main factors controlling litterfall and foliage properties mainly through the changes in photosynthetic capacity and stomatal conductance.

Conclusions

Long-term soil N availabilities and climatic factors were the two of the main driving factors of C and N cycling in the studied forest sites. Further studies are needed to compare soil and litterfall properties before and after burning to profoundly understand the effects of prescribed burning on soil labile C and N variations.

     

The potential of hyperspectral images and partial least square regression for predicting total carbon,total nitrogen and their isotope composition in forest litterfall samples

Purpose

The main objective of this study was to examine the potential of using hyperspectral image analysis for prediction of total carbon (TC), total nitrogen (TN) and their isotope composition (δ¹³C and δ¹⁵N) in forest leaf litterfall samples.

Materials and methods

Hyperspectral images were captured from ground litterfall samples of a natural forest in the spectral range of 400–1700 nm. A partial least-square regression model (PLSR) was used to correlate the relative reflectance spectra with TC, TN, δ¹³C and δ¹⁵N in the litterfall samples. The most important wavelengths were selected using β coefficient, and the final models were developed using the most important wavelengths. The models were, then, tested using an external validation set.

Results and discussion

The results showed that the data of TC and δ¹³C could not be fitted to the PLSR model, possibly due to small variations observed in the TC and δ¹³C data. The model, however, was fitted well to TN and δ¹⁵N. The cross-validation R² _cv of the models for TN and δ¹⁵N were 0.74 and 0.67 with the RMSE_cv of 0.53% and 1.07‰, respectively. The external validation R² _ex of the prediction was 0.64 and 0.67, and the RMSE_ex was 0.53% and 1.19 ‰, for TN and δ¹⁵N, respectively. The ratio of performance to deviation (RPD) of the predictions was 1.48 and 1.53, respectively, for TN and δ¹⁵N, showing that the models were reliable for the prediction of TN and δ¹⁵N in new forest leaf litterfall samples.

Conclusions

The PLSR model was not successful in predicting TC and δ¹³C in forest leaf litterfall samples using hyperspectral data. The predictions of TN and δ¹⁵N values in the external litterfall samples were reliable, and PLSR can be used for future prediction.

     

Impacts of prescribed burnings on litter production,nitrogen concentration, δ13C and δ15N in a suburban eucalypt natural forest of subtropical Australia

Purposes

Prescribed burning is projected to be adopted more frequently with intensifying climate change; thus, a long-term study is necessary to understand the burning impacts on forest productivity and carbon (C) and nitrogen (N) cycling. Litter fall production rate can be used to indicate burning impacts on forest productivity, whereas N concentration, and C and N isotope composition (δ¹³C and δ¹⁵N) can be used to infer burning impacts on C and N cycling in plant-soil system.

Materials and methods

In this study, the impacts of low-intensity prescribed burning on litter production, N concentration, and C and N isotope compositions were continuously investigated for 6 years at five study sites in a natural eucalypt forest of subtropical Australia.

Results and discussion

Higher leaf litter production rate, N concentration and δ¹⁵N, and lower δ¹³C could be seen shortly after prescribed burning. The higher leaf litter N concentration and lower δ¹³C were likely due to the ease of competition for soil N and moisture from understory vegetation in the short term by prescribed burning. Leaf δ¹⁵N and N concentration were closely correlated, and seasonal changes in leaf litter production rate, δ¹³C and δ¹⁵N were observed. Burning season and related severity might determine the suppression degree of understory vegetation. Time since fire (TSF) was a significant impact factor influencing the litter fall production rate, N concentration, δ¹³C and δ¹⁵N of leaf litter fall for a decade following prescribed burning. However, monthly rainfall and temperature were less consistent in their impacts.

Conclusions

Nitrogen limitation was enhanced by prescribed burning through the removal of litter and understory vegetation in the N poor forest and might be responsible for the long-term burning impacts. Low-intensity prescribed burning might have a long-lasting impact on forest litter productivity in nutrient poor forests in subtropical Australia.

     

Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection

Purpose

Fast and real-time prediction of leaf nutrient concentrations can facilitate decision-making for fertilisation regimes on farms and address issues raised with over-fertilisation. Cacao (Theobroma cacao L.) is an important cash crop and requires nutrient supply to maintain yield. This project aimed to use chemometric analysis and wavelength selection to improve the accuracy of foliar nutrient prediction.

Materials and methods

We used a visible-near infrared (400–1000 nm) hyperspectral imaging (HSI) system to predict foliar calcium (Ca), potassium (K), phosphorus (P) and nitrogen (N) of cacao trees. Images were captured from 95 leaf samples. Partial least square regression (PLSR) models were developed to predict leaf nutrient concentrations and wavelength selection was undertaken.

Results and discussion

Using all wavelengths, Ca (R²_CV?=?0.76, RMSE_CV?=?0.28), K (R²_CV?=?0.35, RMSE_CV?=?0.46), P (R²_CV?=?0.75, RMSE_CV?=?0.019) and N (R²_CV?=?0.73, RMSE_CV?=?0.17) were predicted. Wavelength selection increased the prediction accuracy of Ca (R²_CV?=?0.79, RMSE_CV?=?0.27) and N (R²_CV?=?0.74, RMSE_CV?=?0.16), while did not affect prediction accuracy of foliar K (R²_CV?=?0.35, RMSE_CV?=?0.46) and P (R²_CV?=?0.75, RMSE_CV?=?0.019).

Conclusions

Visible-near infrared HSI has a good potential to predict Ca, P and N concentrations in cacao leaf samples, but K concentrations could not be predicted reliably. Wavelength selection increased the prediction accuracy of foliar Ca and N leading to a reduced number of wavelengths involved in developed models.