title: Scientific workflow management with ADAMS: building and data mining a database of crop protection and related data

authors: Reutemann, Peter

subjects: Machine learning

abstract: Data mining is said to be a field that encourages data to speak for itself rather than “forcing” data to conform to a pre-specified model, but we have to acknowledge that what is spoken by the data may well be gibberish. To obtain meaning from data it is important to use techniques systematically, to follow sound experimental procedure and to examine results expertly. This paper presents a framework for scientific discovery from data with two examples from the biological sciences. The first case is a re-investigation of previously published work on aphid trap data to predict aphid phenology and the second is a commercial application for identifying and counting insects captured on sticky plates in greenhouses. Using support vector machines rather than neural networks or linear regression gives better results in case of the aphid trap data. For both cases, we use the open source machine learning workbench WEKA for predictive modelling and the open source ADAMS workflow system for automating data collection, preparation, feature generation, application of predictive models and output generation.

text: Scientific workflow management with ADAMS: building and data mining a database of crop protection and related data Data mining is said to be a field that encourages data to speak for itself rather than “forcing” data to conform to a pre-specified model, but we have to acknowledge that what is spoken by the data may well be gibberish. To obtain meaning from data it is important to use techniques systematically, to follow sound experimental procedure and to examine results expertly. This paper presents a framework for scientific discovery from data with two examples from the biological sciences. The first case is a re-investigation of previously published work on aphid trap data to predict aphid phenology and the second is a commercial application for identifying and counting insects captured on sticky plates in greenhouses. Using support vector machines rather than neural networks or linear regression gives better results in case of the aphid trap data. For both cases, we use the open source machine learning workbench WEKA for predictive modelling and the open source ADAMS workflow system for automating data collection, preparation, feature generation, application of predictive models and output generation.

year: 2015

title: Consumer adoption of blockchain food traceability: effects of innovation-adoption characteristics, expertise in food traceability and blockchain technology, and segmentation

authors: Wang, Ou

abstract: Purpose: This study explores the influence of the following factors on consumer adoption of blockchain food traceability (BFT): innovation-adoption characteristics, segmentation, expertise in food traceability, expertise in blockchain technology, food categorical preferences and perceived important features of BFT. Design/methodology/approach: The data was collected via an online questionnaire with 1,401 participants in New Zealand. Exploratory factor analysis, structural equation modelling and segmentation analysis were undertaken. Findings: Consumer adoption of blockchain food traceability was significantly influenced by two innovation-adoption characteristics – perceived incentives and perceived complexity, as well as their expertise in food traceability. Two consumer segments were identified: Conservatives (48%) and Pioneers (52%). Significant differences were found between these two segments in terms of gender, age, education, occupation, residential area and ethnicity. Consumers are more willing to use BFT for purchasing fresh, imported, staple and normal foods than for processed, domestic and upscale foods. Their perceived important specific features of BFT are product origin, food safety information, quality control, food safety information, hygienic condition and scarcity management. Originality/value: This study contributes knowledge to address the current knowledge gap regarding consumer adoption of blockchain food traceability by using a large sample set. It is also the first study to recognise consumer segments for BFT; to provide information about consumers' important socio-demographic characteristics, food categorical preferences and perceived important features towards BFT; and to explore the influences of consumers' innovation-adoption characteristics, expertise in food traceability and expertise in blockchain technology on their adoption of blockchain food traceability.

text: Consumer adoption of blockchain food traceability: effects of innovation-adoption characteristics, expertise in food traceability and blockchain technology, and segmentation Purpose: This study explores the influence of the following factors on consumer adoption of blockchain food traceability (BFT): innovation-adoption characteristics, segmentation, expertise in food traceability, expertise in blockchain technology, food categorical preferences and perceived important features of BFT. Design/methodology/approach: The data was collected via an online questionnaire with 1,401 participants in New Zealand. Exploratory factor analysis, structural equation modelling and segmentation analysis were undertaken. Findings: Consumer adoption of blockchain food traceability was significantly influenced by two innovation-adoption characteristics – perceived incentives and perceived complexity, as well as their expertise in food traceability. Two consumer segments were identified: Conservatives (48%) and Pioneers (52%). Significant differences were found between these two segments in terms of gender, age, education, occupation, residential area and ethnicity. Consumers are more willing to use BFT for purchasing fresh, imported, staple and normal foods than for processed, domestic and upscale foods. Their perceived important specific features of BFT are product origin, food safety information, quality control, food safety information, hygienic condition and scarcity management. Originality/value: This study contributes knowledge to address the current knowledge gap regarding consumer adoption of blockchain food traceability by using a large sample set. It is also the first study to recognise consumer segments for BFT; to provide information about consumers' important socio-demographic characteristics, food categorical preferences and perceived important features towards BFT; and to explore the influences of consumers' innovation-adoption characteristics, expertise in food traceability and expertise in blockchain technology on their adoption of blockchain food traceability.

year: 2022

title: Lake Rotokakahi water quality modelling

authors: Jones, Hannah Frances Emily

abstract: Lake Rotokakahi is an iwi-owned lake, administered by the Lake Rotokakahi Board of Control. The lake has a surface area of 4.4 km2, a mean depth of 17.5 m, and a catchment of 19.7 km2 of which most is in exotic forest (46.5%), with some pasture (27.8%) and regenerating native forest and scrub (25.7%). Lake Rotokakahi has only one permanent surface inflow and it is assumed that the lake must be predominantly groundwater fed. The lake outflow, Te Wairoa Stream, flows into Lake Tarawera. Monitoring indicates that water quality has recently declined in the lake, with a shift from a mesotrophic state in the 1990's, to currently being classified as eutrophic. Increased algal biomass has reduced water clarity and in May 2011 the first ever recorded algae bloom of a toxic cyanobacterium species resulted in a fish kill in Te Wairoa Stream. With the majority of the catchment in either exotic or native forest, the cause for the decline in water quality is unclear. However there is some concern that forestry harvesting operations may lead to increased surface runoff of sediment and associated phosphorus in ephemeral streams that feed into the lake. The objective of this study was to setup a water quality model for Lake Rotokakahi, which in the future may provide a decisionsupport tool for lake managers.
The model used, DYRESM-CAEDYM, is a one-dimensional (1D) coupled hydrodynamic-water quality model that has been widely used in New Zealand and overseas. Methodology for deriving model forcing data (e.g. meteorological data, inflow volumes and nutrient loads), including the application of catchment and lake water balances, is described in this report. The catchment water balance indicates that groundwater and rainfall account for 37% and 42%, respectively, of the total inflow volume to the lake, with surface and ephemeral inflows contributing a smaller proportion (c. 20% combined). Furthermore, it appears that groundwater contributes a significant proportion (c. 60 - 70%) of the total nutrient loads, although surface and ephemeral inflows also contribute a significant proportion (34%) of the total phosphorus load. It should be noted that the catchment water balance is based on a very limited dataset, and therefore subject to significant uncertainty. 
We simulated the period July 2009 to June 2012 with DYRESM-CAEDYM. Model performance statistics indicated reasonable simulation of water quality for the calibration period, but did not satisfactorily capture the magnitude and dynamics of chlorophyll a and some nutrient species over an independent model validation period. There may be a number of reasons for the poor model performance over the validation period. For example, it may be that zooplankton and/or freshwater mussels (kakahi), which were not included in the model configuration, may exert significant grazing pressure on the phytoplankton populations at certain times. Furthermore, the significant uncertainty in the catchment water and nutrient loads most likely will have affected model performance. Nutrient concentrations are routinely monitored only in the surface inflow. There are no measurements of nutrient and sediment loads in ephemeral streams, and there is very limited data on groundwater nutrient concentrations.
In its current form, the DYRESM-CAEDYM model is not suitable for scenario testing of lake management options. It is recommended that effort is directed at quantification of ephemeral and groundwater inflows and their associated nutrient and (particularly for ephemeral inflows) sediment loads. This may be particularly important for ephemeral inflows located close to the lake that may be influenced by forestry harvesting operations. Recent increases in measured total phosphorus and phosphate concentrations in the surface inflow and in the lake, combined with the degradation of water quality that has already occurred over the last 20 years, provides further impetus for addressing this critical information gap. Were new data to  become available, opportunities exist for improving the performance of the current model and for developing a three-dimensional coupled hydrodynamic-water quality model (e.g. ELCOM-CAEDYM) with representation of kakahi and/or zooplankton, which may be able to resolve questions pertinent to the functioning of the lake, and to lake and catchment management.

text: Lake Rotokakahi water quality modelling Lake Rotokakahi is an iwi-owned lake, administered by the Lake Rotokakahi Board of Control. The lake has a surface area of 4.4 km2, a mean depth of 17.5 m, and a catchment of 19.7 km2 of which most is in exotic forest (46.5%), with some pasture (27.8%) and regenerating native forest and scrub (25.7%). Lake Rotokakahi has only one permanent surface inflow and it is assumed that the lake must be predominantly groundwater fed. The lake outflow, Te Wairoa Stream, flows into Lake Tarawera. Monitoring indicates that water quality has recently declined in the lake, with a shift from a mesotrophic state in the 1990's, to currently being classified as eutrophic. Increased algal biomass has reduced water clarity and in May 2011 the first ever recorded algae bloom of a toxic cyanobacterium species resulted in a fish kill in Te Wairoa Stream. With the majority of the catchment in either exotic or native forest, the cause for the decline in water quality is unclear. However there is some concern that forestry harvesting operations may lead to increased surface runoff of sediment and associated phosphorus in ephemeral streams that feed into the lake. The objective of this study was to setup a water quality model for Lake Rotokakahi, which in the future may provide a decisionsupport tool for lake managers.
The model used, DYRESM-CAEDYM, is a one-dimensional (1D) coupled hydrodynamic-water quality model that has been widely used in New Zealand and overseas. Methodology for deriving model forcing data (e.g. meteorological data, inflow volumes and nutrient loads), including the application of catchment and lake water balances, is described in this report. The catchment water balance indicates that groundwater and rainfall account for 37% and 42%, respectively, of the total inflow volume to the lake, with surface and ephemeral inflows contributing a smaller proportion (c. 20% combined). Furthermore, it appears that groundwater contributes a significant proportion (c. 60 - 70%) of the total nutrient loads, although surface and ephemeral inflows also contribute a significant proportion (34%) of the total phosphorus load. It should be noted that the catchment water balance is based on a very limited dataset, and therefore subject to significant uncertainty. 
We simulated the period July 2009 to June 2012 with DYRESM-CAEDYM. Model performance statistics indicated reasonable simulation of water quality for the calibration period, but did not satisfactorily capture the magnitude and dynamics of chlorophyll a and some nutrient species over an independent model validation period. There may be a number of reasons for the poor model performance over the validation period. For example, it may be that zooplankton and/or freshwater mussels (kakahi), which were not included in the model configuration, may exert significant grazing pressure on the phytoplankton populations at certain times. Furthermore, the significant uncertainty in the catchment water and nutrient loads most likely will have affected model performance. Nutrient concentrations are routinely monitored only in the surface inflow. There are no measurements of nutrient and sediment loads in ephemeral streams, and there is very limited data on groundwater nutrient concentrations.
In its current form, the DYRESM-CAEDYM model is not suitable for scenario testing of lake management options. It is recommended that effort is directed at quantification of ephemeral and groundwater inflows and their associated nutrient and (particularly for ephemeral inflows) sediment loads. This may be particularly important for ephemeral inflows located close to the lake that may be influenced by forestry harvesting operations. Recent increases in measured total phosphorus and phosphate concentrations in the surface inflow and in the lake, combined with the degradation of water quality that has already occurred over the last 20 years, provides further impetus for addressing this critical information gap. Were new data to  become available, opportunities exist for improving the performance of the current model and for developing a three-dimensional coupled hydrodynamic-water quality model (e.g. ELCOM-CAEDYM) with representation of kakahi and/or zooplankton, which may be able to resolve questions pertinent to the functioning of the lake, and to lake and catchment management.

year: 2014