Abstract Purpose Revegetation of riparian zones is important to improve their soil nitrogen (N) dynamics and to preserve their microbial compositions. However, the success of revegetation projects currently depends on weed control to reduce non-target vegetation competing over nutrients and to ensure the target plant species growth and survival. Different weed control methods affect soil microbial composition and N cycling. However, the long-term effects of herbicides on soil nitrogen (N) pools and microbial community composition remain uncertain even after cessation of the herbicide application. Materials and methods This study compared the impacts of different herbicides (Roundup ® , BioWeed™, Slasher ® , and acetic acid) with mulch on soil N dynamics and microbial community structure 3 years after vegetation establishment (herbicides applied repeatedly in the first 2 years after which no herbicides were applied in the third final year). Results and discussion Soil microbial biomass carbon (MBC) was significantly higher in mulch compared with Roundup ® , BioWeed™, Slasher ® , and acetic acid at month 26 at the Kandanga site and month 10 at the Pinbarren site. Soil MBC remained significantly higher in mulch compared with Roundup ® and BioWeed™, 12 months after the cessation of herbicide application at the Pinbarren site. Soil MBC in the Roundup ® and BioWeed™ groups was also lower than the acceptable threshold (160 mg kg −1 ) at month 34 at the Pinbarren site. Soil NO 3 − -N was significantly higher in the mulch than the Roundup ® at months 22 and 34 after revegetation at the Pinbarren site which could be partly explained by the decreased abundance of the denitrifying bacteria ( Candidatus solibacter and C. koribacter ). Additionally, both soil bacterial and fungal communities at the Pinbarren site and only fungal community at the Kandanga site were different in the mulch group compared with all other herbicides. The differences persisted 12 months after the cessation of herbicide application at the Pinbarren site. Conclusion Our study suggested that the application of mulch to assist with riparian revegetation would be beneficial for soil microbial functionality. The use of herbicides may have long-lasting effects on soil microbial biomass and diversity and therefore herbicides should be used with caution as part of an integrated land management plan.
Hyperspectral image (HSI) analysis has the potential to estimate organic compounds in plants and foods. Curcumin is an important compound used to treat a range of medical conditions. Therefore, a method to rapidly determine rhizomes with high curcumin content on-farm would be of significant advantage for farmers. Curcumin content of rhizomes varies within, and between varieties but current chemical analysis methods are expensive and time consuming. This study compared curcumin in three turmeric (Curcuma longa) varieties and examined the potential for laboratory-based HSI to rapidly predict curcumin using the visible–near infrared (400–1000 nm) spectrum. Hyperspectral images (n = 152) of the fresh rhizome outer-skin and flesh were captured, using three local varieties (yellow, orange, and red). Distribution of curcuminoids and total curcumin was analysed. Partial least squares regression (PLSR) models were developed to predict total curcumin concentrations. Total curcumin and the proportion of three curcuminoids differed significantly among all varieties. Red turmeric had the highest total curcumin concentration (0.83 ± 0.21%) compared with orange (0.37 ± 0.12%) and yellow (0.02 ± 0.02%). PLSR models predicted curcumin using raw spectra of rhizome flesh and pooled data for all three varieties (R2c = 0.83, R2p = 0.55, ratio of prediction to deviation (RPD) = 1.51) and was slightly improved by using images of a single variety (orange) only (R2c = 0.85, R2p = 0.62, RPD = 1.65). However, prediction of curcumin using outer-skin of rhizomes was poor (R2c = 0.64, R2p = 0.37, RPD = 1.28). These models can discriminate between ‘low’ and ‘high’ values and so may be adapted into a two-level grading system. HSI has the potential to help identify turmeric rhizomes with high curcumin concentrations and allow for more efficient refinement into curcumin for medicinal purposes.
Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes.
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