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Peter J. Thorburn
Commonwealth Scientific and Industrial Research Organisation
AgricultureSoil waterEnvironmental scienceAgroforestryAgronomy
240Publications
43H-index
6,705Citations
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Publications 249
Newest
#1Yi-Fan ZhangH-Index: 3
#2Peter FitchH-Index: 8
Last. Peter J. ThorburnH-Index: 43
view all 3 authors...
Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack ...
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#1E. A. MeierH-Index: 7
#2Peter J. ThorburnH-Index: 43
Last. Jody S. BiggsH-Index: 9
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The agricultural sector has potential to provide greenhouse gas (GHG) mitigation by sequestering soil organic carbon (SOC). Replacing cropland with permanent pasture is one practice promoted for its potential to sequester soil carbon. However, pastures frequently support livestock, which produce other GHG emissions that could negate the abatement from increased SOC, especially given the declining rate of SOC sequestration through time. Our purpose was to determine whether the abatement provided ...
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#1Peter J. ThorburnH-Index: 43
#2Brian KeatingH-Index: 35
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#1Yi-Fan Zhang (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 3
#2Peter J. Thorburn (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 43
Last. Peter Fitch (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 8
view all 3 authors...
Predicting the trend of water quality is essential in environmental management decision support systems. Despite various data-driven models in water quality prediction, most studies focus on predicting a single water quality variable. When multiple water quality variables need to be estimated, preparing several data-driven models may require unaffordable computing resources. Also, the changing patterns of several water quality variables can only be revealed by processing long term historical obs...
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#1Emma Jakku (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 9
#2Bruce V. Taylor (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 47
Last. Peter J. Thorburn (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 43
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Abstract Advances in Smart Farming and Big Data applications have the potential to help agricultural industries meet productivity and sustainability challenges. However, these benefits are unlikely to be realised if the social implications of these technological innovations are not adequately considered by those who promote them. Big Data applications are intrinsically socio-technical; their development and deployment are a product of social interactions between people, institutional and regulat...
6 CitationsSource
#1Yuri Shendryk (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 1
#2Yannik Rist (CSIRO: Commonwealth Scientific and Industrial Research Organisation)
Last. Peter J. Thorburn (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 43
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Abstract With the increasing availability of high-resolution satellite imagery it is important to improve the efficiency and accuracy of satellite image indexing, retrieval and classification. Furthermore, there is a need for utilizing all available satellite imagery in identifying general land cover types and monitoring their changes through time irrespective of their spatial, spectral, temporal and radiometric resolutions. Therefore, in this study, we developed deep learning models able to eff...
2 CitationsSource
#1Daniel Wallach (INRA: Institut national de la recherche agronomique)H-Index: 29
#2Taru PalosuoH-Index: 30
Last. Yan ZhuH-Index: 60
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Plant phenology, which describes the timing of plant development, is a major aspect of plant response to environment and for crops, a major determinant of yield. Since climate change is projected to alter crop phenology worldwide, there is a large effort to predict phenology as a function of environment. Many studies have focused on comparing model equations for describing how phenology responds to weather but the effect of crop model calibration, also expected to be important, has received much...
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#1M.P. Vilas (CSIRO: Commonwealth Scientific and Industrial Research Organisation)
#2K. Verburg (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 14
Last. Graham D. Bonnett (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 26
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Abstract Nitrification inhibitors show great potential to reduce nitrogen losses from agricultural systems and to improve nitrogen use efficiency. The most recently developed nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP) is gaining popularity due to its benefits relative to other compounds. However, the behaviour of DMPP and its effect on nitrification in soils has been characterised using inconsistent and confusing terminology. Many studies have used the term half-life to descri...
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#1Peter J. ThorburnH-Index: 43
#2Freeman J. CookH-Index: 24
Last. Keith L. BristowH-Index: 33
view all 3 authors...
#1Yi-Fan Zhang (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 1
#2Peter Fitch (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 8
Last. Peter J. Thorburn (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 43
view all 4 authors...
Predicting {\color{red}{trends in water quality}} plays an essential role in the field of environmental modelling. Though artificial neural networks (ANN) have been involved in predicting water quality in many studies, the prediction performance is highly affected by the model's inputs and neural network structure. Many researchers selected water quality variables based on Pearson correlation. However, this kind of method can only capture linear dependencies. Moreover, when dealing with multivar...
1 CitationsSource
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