title: Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness

authors: Kamphuis, C

subjects: Science & Technology

abstract: The hypothesis was that sensors currently available on farm that monitor behavioral and physiological characteristics have potential for the detection of lameness in dairy cows. This was tested by applying additive logistic regression to variables derived from sensor data. Data were collected between November 2010 and June 2012 on 5 commercial pasture-based dairy farms. Sensor data from weigh scales (liveweight), pedometers (activity), and milk meters (milking order, unadjusted and adjusted milk yield in the first 2 min of milking, total milk yield, and milking duration) were collected at every milking from 4,904 cows. Lameness events were recorded by farmers who were trained in detecting lameness before the study commenced. A total of 318 lameness events affecting 292 cows were available for statistical analyses. For each lameness event, the lame cow’s sensor data for a time period of 14 d before observation date were randomly matched by farm and date to 10 healthy cows (i.e., cows that were not lame and had no other health event recorded for the matched time period). Sensor data relating to the 14-d time periods were used for developing univariable (using one source of sensor data) and multivariable (using multiple sources of sensor data) models. Model development involved the use of additive logistic regression by applying the LogitBoost algorithm with a regression tree as base learner. The model’s output was a probability estimate for lameness, given the sensor data collected during the 14-d time period. Models were validated using leave-one-farm-out cross-validation and, as a result of this validation, each cow in the data set (318 lame and 3,180 nonlame cows) received a probability estimate for lameness. Based on the area under the curve (AUC), results indicated that univariable models had low predictive potential, with the highest AUC values found for liveweight (AUC = 0.66), activity (AUC = 0.60), and milking order (AUC = 0.65). Combining these 3 sensors improved AUC to 0.74. Detection performance of this combined model varied between farms but it consistently and significantly outperformed univariable models across farms at a fixed specificity of 80%. Still, detection performance was not high enough to be implemented in practice on large, pasture-based dairy farms. Future research may improve performance by developing variables based on sensor data of liveweight, activity, and milking order, but that better describe changes in sensor data patterns when cows go lame.

text: Applying additive logistic regression to data derived from sensors monitoring behavioral and physiological characteristics of dairy cows to detect lameness The hypothesis was that sensors currently available on farm that monitor behavioral and physiological characteristics have potential for the detection of lameness in dairy cows. This was tested by applying additive logistic regression to variables derived from sensor data. Data were collected between November 2010 and June 2012 on 5 commercial pasture-based dairy farms. Sensor data from weigh scales (liveweight), pedometers (activity), and milk meters (milking order, unadjusted and adjusted milk yield in the first 2 min of milking, total milk yield, and milking duration) were collected at every milking from 4,904 cows. Lameness events were recorded by farmers who were trained in detecting lameness before the study commenced. A total of 318 lameness events affecting 292 cows were available for statistical analyses. For each lameness event, the lame cow’s sensor data for a time period of 14 d before observation date were randomly matched by farm and date to 10 healthy cows (i.e., cows that were not lame and had no other health event recorded for the matched time period). Sensor data relating to the 14-d time periods were used for developing univariable (using one source of sensor data) and multivariable (using multiple sources of sensor data) models. Model development involved the use of additive logistic regression by applying the LogitBoost algorithm with a regression tree as base learner. The model’s output was a probability estimate for lameness, given the sensor data collected during the 14-d time period. Models were validated using leave-one-farm-out cross-validation and, as a result of this validation, each cow in the data set (318 lame and 3,180 nonlame cows) received a probability estimate for lameness. Based on the area under the curve (AUC), results indicated that univariable models had low predictive potential, with the highest AUC values found for liveweight (AUC = 0.66), activity (AUC = 0.60), and milking order (AUC = 0.65). Combining these 3 sensors improved AUC to 0.74. Detection performance of this combined model varied between farms but it consistently and significantly outperformed univariable models across farms at a fixed specificity of 80%. Still, detection performance was not high enough to be implemented in practice on large, pasture-based dairy farms. Future research may improve performance by developing variables based on sensor data of liveweight, activity, and milking order, but that better describe changes in sensor data patterns when cows go lame.

year: 2013-11-01

title: 3D Shape Reconstruction from Multiple Range Image Views

authors: Ganapathi Annadurai, Kartick

subjects: shape reconstruction

abstract: Shape reconstruction of different three dimensional objects using multiple range images has evolved recently within the recent past. In this research shape reconstruction of a three dimensional object using multiple range image views is investigated.
Range images were captured using the Waikato Range Imager. This range images camera is novel in that it uses heterodyne imaging and is capable of acquiring range images with precision less than a millimeter simultaneously over a full field. Multiple views of small objects were taken and the FastRBF was explored as a mean of registration and surface rendering. For comparison to the real range data, simulated range data under noise free condition were also generated and reconstructed with the FastRBF tool box.
The registration and reconstruction of simple object was performed using different views with the FastRBF toolbox. Analysis of the registration process showed that the translation error produced due to distortion during registration of different views hinders the process of reconstructing a complete surface. While analyzing the shape reconstruction using the FastRBF tool it is also determined that a small change in accuracy values can affect the interpolation drastically. Results of reconstruction of a real 3D object from multiple views are shown.

text: 3D Shape Reconstruction from Multiple Range Image Views Shape reconstruction of different three dimensional objects using multiple range images has evolved recently within the recent past. In this research shape reconstruction of a three dimensional object using multiple range image views is investigated.
Range images were captured using the Waikato Range Imager. This range images camera is novel in that it uses heterodyne imaging and is capable of acquiring range images with precision less than a millimeter simultaneously over a full field. Multiple views of small objects were taken and the FastRBF was explored as a mean of registration and surface rendering. For comparison to the real range data, simulated range data under noise free condition were also generated and reconstructed with the FastRBF tool box.
The registration and reconstruction of simple object was performed using different views with the FastRBF toolbox. Analysis of the registration process showed that the translation error produced due to distortion during registration of different views hinders the process of reconstructing a complete surface. While analyzing the shape reconstruction using the FastRBF tool it is also determined that a small change in accuracy values can affect the interpolation drastically. Results of reconstruction of a real 3D object from multiple views are shown.

year: 2006

title: Phytoplankton nutrient limitation in Lake Rotorua

authors: Tempero, Grant Wayne

abstract: Lake Rotorua is one of 12 lakes jointly managed by the Rotorua Lakes Council, Te Arawa Lakes Trust and the Bay of Plenty Regional Council under the Rotorua Te Arawa Lakes Programme. The lake is currently classified as eutrophic and a Trophic Lake Index (TLI) value of 4.2 has been set as an integrated target for lake water quality. A number of catchment and lake management strategies have been implemented to achieve the targeted TLI including land use changes, wetland enhancement, lake weed harvesting, sediment detainment bunds, sewage reticulation and alum dosing. Alum (aluminium sulphate) is commonly applied to freshwater and wastewater systems to remove phosphorus, reducing its availability for phytoplankton growth. The initiation of alum dosing to the Utuhina Stream in 2006 and the Puarenga Stream in 2010 was associated with a significant decline in water column dissolved reactive phosphorus (DRP) and improvements in water quality of Lake Rotorua. 
The use of alum to control external and internal phosphorus loading has been identified as key component in the restoration of Lake Rotorua and recently observed improvements in water quality suggest that the lake could be transitioning to a more phosphorus limited state. Determination of phytoplankton nutrient limitation in Lake Rotorua was identified as a priority through the 2017 Plan Change 10 Science Review. An initial seasonal nutrient limitation study was carried out in 2019 with nutrient limitation assays conducted at 3-monthly intervals over the course of the year. Nitrogen limitation was indicated for the March assay and nutrient co-limitation for the December period, but results for the winter and spring periods were inconclusive. There has also been increasing recognition that nutrient availability in limnetic systems may vary in response to seasonal changes in external and external loading and even to isolated storm events. 
The University of Waikato was contracted to investigate potential changes in nutrient limitation within Lake Rotorua at sub-monthly time-scales, and to determine if alum dose rates could be optimised to take advantage of periods of phosphorus limitation. Phytoplankton nutrient limitation growth assays were conducted on a fortnightly basis from mid-October 2020 to May 2021. In addition to determination of lake surface nutrient and chlorophyll a concentrations, triplicate 1 L phytoplankton samples were spiked with either phosphorus (0.1 mg-P L⁻¹), nitrogen (1 mg-N L⁻¹) or phosphorus (0.1 mg-P L⁻¹) and nitrogen (1 mg-N L⁻¹) and incubated for 5-days under a single light intensity of 100 μmol m⁻²s⁻¹ PAR at lake surface temperature and photoperiod. Phytoplankton growth responses were measured as comparative chlorophyll a, comparative biomass and net biomass growth. Summarised results from the 16 assays along with the predicted nutrient state from the stoichiometric mass ratio of TN:TP are presented in the table below. 
Under the test conditions nitrogen limitation was indicated for most of the summer months (December–March) and nutrient co-limitation during the spring and late autumn. Only short (~2 weeks) periods of P-limitation were observed, one of which was associated with an intensive storm event at the end of March. These seasonal fluctuations in nitrogen and phosphorus supply are similar to those observed in other temperate limnetic systems. In the spring, external nitrogen and phosphorus loading is high, then decline in the summer as rainfall and associated inflows wane. During the summer, internal loading becomes more important in polymictic lakes due to repeated internal nutrient cycling from stratification and mixing events. This results in phosphorus limitation in the spring, transitioning to nitrogen limitation or colimitation (primarily nitrogen) in the summer, and then returning to phosphorus/colimitation during the winter due to increased nitrogen-loading from the catchment from increased catchment discharge. 
Due to the modest periods of phosphorus limitation indicated by the assays, there appears to be little potential for optimisation of alum dose rates in order capitalise on natural periods of phosphorus limitation. Further, changes in phytoplankton community composition and abundance appear to be more strongly driven by physical environmental factors such as temperature, light and wind speed, than nutrient availability. Although alum dosing is effective in removing DRP from the Utuhina and Puarenga inflows, its assumed effectiveness in reducing internal phosphorus loading in Lake Rotorua requires further evaluation.

text: Phytoplankton nutrient limitation in Lake Rotorua Lake Rotorua is one of 12 lakes jointly managed by the Rotorua Lakes Council, Te Arawa Lakes Trust and the Bay of Plenty Regional Council under the Rotorua Te Arawa Lakes Programme. The lake is currently classified as eutrophic and a Trophic Lake Index (TLI) value of 4.2 has been set as an integrated target for lake water quality. A number of catchment and lake management strategies have been implemented to achieve the targeted TLI including land use changes, wetland enhancement, lake weed harvesting, sediment detainment bunds, sewage reticulation and alum dosing. Alum (aluminium sulphate) is commonly applied to freshwater and wastewater systems to remove phosphorus, reducing its availability for phytoplankton growth. The initiation of alum dosing to the Utuhina Stream in 2006 and the Puarenga Stream in 2010 was associated with a significant decline in water column dissolved reactive phosphorus (DRP) and improvements in water quality of Lake Rotorua. 
The use of alum to control external and internal phosphorus loading has been identified as key component in the restoration of Lake Rotorua and recently observed improvements in water quality suggest that the lake could be transitioning to a more phosphorus limited state. Determination of phytoplankton nutrient limitation in Lake Rotorua was identified as a priority through the 2017 Plan Change 10 Science Review. An initial seasonal nutrient limitation study was carried out in 2019 with nutrient limitation assays conducted at 3-monthly intervals over the course of the year. Nitrogen limitation was indicated for the March assay and nutrient co-limitation for the December period, but results for the winter and spring periods were inconclusive. There has also been increasing recognition that nutrient availability in limnetic systems may vary in response to seasonal changes in external and external loading and even to isolated storm events. 
The University of Waikato was contracted to investigate potential changes in nutrient limitation within Lake Rotorua at sub-monthly time-scales, and to determine if alum dose rates could be optimised to take advantage of periods of phosphorus limitation. Phytoplankton nutrient limitation growth assays were conducted on a fortnightly basis from mid-October 2020 to May 2021. In addition to determination of lake surface nutrient and chlorophyll a concentrations, triplicate 1 L phytoplankton samples were spiked with either phosphorus (0.1 mg-P L⁻¹), nitrogen (1 mg-N L⁻¹) or phosphorus (0.1 mg-P L⁻¹) and nitrogen (1 mg-N L⁻¹) and incubated for 5-days under a single light intensity of 100 μmol m⁻²s⁻¹ PAR at lake surface temperature and photoperiod. Phytoplankton growth responses were measured as comparative chlorophyll a, comparative biomass and net biomass growth. Summarised results from the 16 assays along with the predicted nutrient state from the stoichiometric mass ratio of TN:TP are presented in the table below. 
Under the test conditions nitrogen limitation was indicated for most of the summer months (December–March) and nutrient co-limitation during the spring and late autumn. Only short (~2 weeks) periods of P-limitation were observed, one of which was associated with an intensive storm event at the end of March. These seasonal fluctuations in nitrogen and phosphorus supply are similar to those observed in other temperate limnetic systems. In the spring, external nitrogen and phosphorus loading is high, then decline in the summer as rainfall and associated inflows wane. During the summer, internal loading becomes more important in polymictic lakes due to repeated internal nutrient cycling from stratification and mixing events. This results in phosphorus limitation in the spring, transitioning to nitrogen limitation or colimitation (primarily nitrogen) in the summer, and then returning to phosphorus/colimitation during the winter due to increased nitrogen-loading from the catchment from increased catchment discharge. 
Due to the modest periods of phosphorus limitation indicated by the assays, there appears to be little potential for optimisation of alum dose rates in order capitalise on natural periods of phosphorus limitation. Further, changes in phytoplankton community composition and abundance appear to be more strongly driven by physical environmental factors such as temperature, light and wind speed, than nutrient availability. Although alum dosing is effective in removing DRP from the Utuhina and Puarenga inflows, its assumed effectiveness in reducing internal phosphorus loading in Lake Rotorua requires further evaluation.

year: 2022