title: Insolvency and economic development: Regional variation and adjustment

authors: Fabling, Richard

subjects: insolvency

abstract: We examine the determinants of forced insolvency in New Zealand. The study incorporates three key features. First, we use regional as well as national data. Second, we analyse the role of property prices, which influence collateral values. Third, we explain the rate of total forced insolvency including personal bankruptcies and involuntary company liquidations. Insolvencies are explained by economic activity, financial variables and collateral values. The interactions between economic activity, leverage and property price (collateral) shocks indicate that region-specific shocks can compound into significant localised economic cycles.

text: Insolvency and economic development: Regional variation and adjustment We examine the determinants of forced insolvency in New Zealand. The study incorporates three key features. First, we use regional as well as national data. Second, we analyse the role of property prices, which influence collateral values. Third, we explain the rate of total forced insolvency including personal bankruptcies and involuntary company liquidations. Insolvencies are explained by economic activity, financial variables and collateral values. The interactions between economic activity, leverage and property price (collateral) shocks indicate that region-specific shocks can compound into significant localised economic cycles.

year: 2005

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: Evaluating the effects of invasive brown bullhead catfish (Ameiurus nebulosus) on kōura (freshwater crayfish, Paranephrops planifrons) in Lake Rotoiti

authors: Francis, Laura Beverley

subjects: catfish

abstract: Brown bullhead catfish (Ameiurus nebulosus) are opportunistic carnivores native to North America and were first detected in Te Weta Bay, Lake Rotoiti, in March 2016. A cordon was placed across the bay to try and contain catfish, but they had already established populations elsewhere. Spread of catfish in the lake raised concern for resident populations of freshwater crayfish or kōura (Paranephrops planifrons), which are of ecological and cultural significance to Māori. Given the limited knowledge of the potential impacts of catfish on native biota, this thesis aims to evaluate the effects of catfish on kōura in Lake Rotoiti. To do this, Bay of Plenty Regional Council (BOPRC) routine fyke netting data was used to explore species trends and relationships. A habitat survey was also conducted using whakaweku (bracken fern bundles) and fyke nets, to generate data on species’ habitat preferences and kōura metrics (e.g., sizes and sex ratios). In addition to fishing, kōura and catfish stomachs were dissected to determine diet. Potential food items of catfish were also collected for stable isotope analysis to estimate dietary overlap between catfish and kōura, and long-term resource acquisition. 

Routine BOPRC fyke netting results indicated that kōura catch per unit effort (CPUE) has declined in Lake Rotoiti, with mean catch rates dropping from 10.6 kōura net-1 night-1 in 2016 to 4.2 kōura net-1 night-1 in 2018. Over the same period, catfish CPUE has increased in the lake, with the highest catch rates in Te Weta Bay, where mean catch rates have increased from 1.1 catfish net-1 night-1 in 2016 to 63.7 catfish net-1 night-1 in 2018. Catfish catch rates were negatively associated with catch rates of kōura (r = −0.180). Mean catfish density also had a significant effect on kōura catch rates, with significantly more kōura being caught at sites without catfish. The negative association between catfish and kōura could be due to catfish eating or competing with kōura, or their differing habitat preferences. Catfish were positively associated with sites that are weedy or muddy and kōura were negatively associated with muddy habitats and were positively associated with rocky habitats. Whakaweku set at shallow depths <1 m in the lake littoral zone were ineffective at catching kōura during the habitat survey. 

Catfish diet consisted largely of chironomid larvae (Chironomidae), detritus, common bullies (Gobiomorphus cotidianus), and to a lesser extent kōura. Kōura were found in 12% of large catfish (>200 mm fork length; FL) stomachs analysed and contributed 5% volumetrically to large catfish diet. Unfortunately, the contribution of kōura to catfish diet could not be established using stable isotopes because isotopic signatures of kōura and bullies were too close to differentiate. Kōura diet consisted primarily of animal remains (common bullies and invertebrates) and detritus. Stable isotopes of carbon and nitrogen revealed that diets of kōura and catfish overlapped, with kōura sharing more of their diet with catfish than vice versa, likely reflecting catfish’s broad diet. This study suggests that catfish are responsible for the recent decline in kōura CPUE in Lake Rotoiti and that catfish are directly and indirectly affecting kōura through predation and competition for shared food resources.

text: Evaluating the effects of invasive brown bullhead catfish (Ameiurus nebulosus) on kōura (freshwater crayfish, Paranephrops planifrons) in Lake Rotoiti Brown bullhead catfish (Ameiurus nebulosus) are opportunistic carnivores native to North America and were first detected in Te Weta Bay, Lake Rotoiti, in March 2016. A cordon was placed across the bay to try and contain catfish, but they had already established populations elsewhere. Spread of catfish in the lake raised concern for resident populations of freshwater crayfish or kōura (Paranephrops planifrons), which are of ecological and cultural significance to Māori. Given the limited knowledge of the potential impacts of catfish on native biota, this thesis aims to evaluate the effects of catfish on kōura in Lake Rotoiti. To do this, Bay of Plenty Regional Council (BOPRC) routine fyke netting data was used to explore species trends and relationships. A habitat survey was also conducted using whakaweku (bracken fern bundles) and fyke nets, to generate data on species’ habitat preferences and kōura metrics (e.g., sizes and sex ratios). In addition to fishing, kōura and catfish stomachs were dissected to determine diet. Potential food items of catfish were also collected for stable isotope analysis to estimate dietary overlap between catfish and kōura, and long-term resource acquisition. 

Routine BOPRC fyke netting results indicated that kōura catch per unit effort (CPUE) has declined in Lake Rotoiti, with mean catch rates dropping from 10.6 kōura net-1 night-1 in 2016 to 4.2 kōura net-1 night-1 in 2018. Over the same period, catfish CPUE has increased in the lake, with the highest catch rates in Te Weta Bay, where mean catch rates have increased from 1.1 catfish net-1 night-1 in 2016 to 63.7 catfish net-1 night-1 in 2018. Catfish catch rates were negatively associated with catch rates of kōura (r = −0.180). Mean catfish density also had a significant effect on kōura catch rates, with significantly more kōura being caught at sites without catfish. The negative association between catfish and kōura could be due to catfish eating or competing with kōura, or their differing habitat preferences. Catfish were positively associated with sites that are weedy or muddy and kōura were negatively associated with muddy habitats and were positively associated with rocky habitats. Whakaweku set at shallow depths <1 m in the lake littoral zone were ineffective at catching kōura during the habitat survey. 

Catfish diet consisted largely of chironomid larvae (Chironomidae), detritus, common bullies (Gobiomorphus cotidianus), and to a lesser extent kōura. Kōura were found in 12% of large catfish (>200 mm fork length; FL) stomachs analysed and contributed 5% volumetrically to large catfish diet. Unfortunately, the contribution of kōura to catfish diet could not be established using stable isotopes because isotopic signatures of kōura and bullies were too close to differentiate. Kōura diet consisted primarily of animal remains (common bullies and invertebrates) and detritus. Stable isotopes of carbon and nitrogen revealed that diets of kōura and catfish overlapped, with kōura sharing more of their diet with catfish than vice versa, likely reflecting catfish’s broad diet. This study suggests that catfish are responsible for the recent decline in kōura CPUE in Lake Rotoiti and that catfish are directly and indirectly affecting kōura through predation and competition for shared food resources.

year: 2019