title: Spatial variation in 'Hayward' kiwifruit fruit size and orchard yield within a growing region across seasons

authors: Woodward, Tim J.

subjects: Fruit size

abstract: Between-orchard variation in average fruit size and yield at commercial harvest of 341 conventionally managed commercial 'Hayward' kiwifruit orchards within the Te Puke growing region of New Zealand was spatially modelled over consecutive growing seasons (2003-08). The magnitude of between-orchard variation in average yield was three-fold higher than that of between-orchard variation in average fruit size, both within and across seasons. Within seasons there were spatial patterns to the distribution of average fruit size and yields between orchards. The temporal consistency of the spatial variation enabled segregation of the Te Puke growing region into geographic zones containing orchards that produced statistically distinct fruit size and yield outcomes at harvest in five out of the six seasons studied. Although the location of an orchard within the Te Puke growing region had an effect on the yield and average size of the fruit produced, orchard location was only weakly predictive of average fruit size and yield outcomes. Therefore we conclude that, in this situation, zonation between orchards should not be where the effort in managing variation in 'Hayward' kiwifruit orchard productivity is concentrated

text: Spatial variation in 'Hayward' kiwifruit fruit size and orchard yield within a growing region across seasons Between-orchard variation in average fruit size and yield at commercial harvest of 341 conventionally managed commercial 'Hayward' kiwifruit orchards within the Te Puke growing region of New Zealand was spatially modelled over consecutive growing seasons (2003-08). The magnitude of between-orchard variation in average yield was three-fold higher than that of between-orchard variation in average fruit size, both within and across seasons. Within seasons there were spatial patterns to the distribution of average fruit size and yields between orchards. The temporal consistency of the spatial variation enabled segregation of the Te Puke growing region into geographic zones containing orchards that produced statistically distinct fruit size and yield outcomes at harvest in five out of the six seasons studied. Although the location of an orchard within the Te Puke growing region had an effect on the yield and average size of the fruit produced, orchard location was only weakly predictive of average fruit size and yield outcomes. Therefore we conclude that, in this situation, zonation between orchards should not be where the effort in managing variation in 'Hayward' kiwifruit orchard productivity is concentrated

year: 2012

title: Improving on bagging with input smearing

authors: Frank, Eibe

subjects: computer science

abstract: Bagging is an ensemble learning method that has proved to be a useful tool in the arsenal of machine learning practitioners. Commonly applied in conjunction with decision tree learners to build an ensemble of decision trees, it often leads to reduced errors in the predictions when compared to using a single tree. A single tree is built from a training set of size N. Bagging is based on the idea that, ideally, we would like to eliminate the variance due to a particular training set by combining trees built from all training sets of size N. However, in practice, only one training set is available, and bagging simulates this platonic method by sampling with replacement from the original training data to form new training sets. In this paper we pursue the idea of sampling from a kernel density estimator of the underlying distribution to form new training sets, in addition to sampling from the data itself. This can be viewed as “smearing out” the resampled training data to generate new datasets, and the amount of “smear” is controlled by a parameter. We show that the resulting method, called “input smearing”, can lead to improved results when compared to bagging. We present results for both classification and regression problems.

text: Improving on bagging with input smearing Bagging is an ensemble learning method that has proved to be a useful tool in the arsenal of machine learning practitioners. Commonly applied in conjunction with decision tree learners to build an ensemble of decision trees, it often leads to reduced errors in the predictions when compared to using a single tree. A single tree is built from a training set of size N. Bagging is based on the idea that, ideally, we would like to eliminate the variance due to a particular training set by combining trees built from all training sets of size N. However, in practice, only one training set is available, and bagging simulates this platonic method by sampling with replacement from the original training data to form new training sets. In this paper we pursue the idea of sampling from a kernel density estimator of the underlying distribution to form new training sets, in addition to sampling from the data itself. This can be viewed as “smearing out” the resampled training data to generate new datasets, and the amount of “smear” is controlled by a parameter. We show that the resulting method, called “input smearing”, can lead to improved results when compared to bagging. We present results for both classification and regression problems.

year: 2006

title: Nā Wai Koe? Our tūpuna call us home: Maremare whānau thinking about connections to whenua tupuna

authors: Sampson, Jasmine

subjects: Whenua Tūpuna

abstract: This research focuses on the Maremare whānau from Tiniwaitara Marae, Turakina, in order to explore the importance of our relationship with our whenua tupuna and the related concept of returning home. 

Although many academics have carefully attended to the concept of ‘disconnection’ from whenua tupuna in order to describe the effects of Māori mobility, this term unfortunately reinforces the colonial attempt to detach our people from the things that provide us with our sense of belonging. I argue that thinking about relationship with whenua tupuna through terms like ‘disconnection’ and ‘reconnection’ are unhelpful and problematic; instead, when we understand this relationship in Māori terms, our connection to our whenua tupuna is an everlasting relationship that is woven into the fabric of our whakapapa.

The research combines interviews with three generations of the Maremare whānau, including people with diverse experiences of relationships with Turakina, with critical reflection on relevant concepts from Te Ao Māori. Specifically, mobilising a Kaupapa Māori Theory approach, the thesis draws on an oriori, ‘He Oriori mō Wharaurangi’ as the methodology and foundation of this thesis. In this way, the thesis both argues and demonstrates the value of researchers, and in particular kairangahau Māori, look to mātauranga Māori to guide us in our research about Māori topics. This oriori is iwi specific, it holds kōrero tuku iho that pertains to my whakapapa as an uri of Ngā Wairiki Ngāti Apa and it is very relevant to both my kaupapa and the people participating in the rangahau.

Ultimately, whakapapa rests at the core of connection to whenua tupuna. It is the foundation in which we stand and it connects us as a people to the land that birthed us. This thesis looks at the different ways that our tūpuna call us home, the different ‘returning home’ experiences within the Maremare whānau and how people within the whānau view their connection with our whenua tupuna in Turakina. Nā wai koe? Our tūpuna call us home.

text: Nā Wai Koe? Our tūpuna call us home: Maremare whānau thinking about connections to whenua tupuna This research focuses on the Maremare whānau from Tiniwaitara Marae, Turakina, in order to explore the importance of our relationship with our whenua tupuna and the related concept of returning home. 

Although many academics have carefully attended to the concept of ‘disconnection’ from whenua tupuna in order to describe the effects of Māori mobility, this term unfortunately reinforces the colonial attempt to detach our people from the things that provide us with our sense of belonging. I argue that thinking about relationship with whenua tupuna through terms like ‘disconnection’ and ‘reconnection’ are unhelpful and problematic; instead, when we understand this relationship in Māori terms, our connection to our whenua tupuna is an everlasting relationship that is woven into the fabric of our whakapapa.

The research combines interviews with three generations of the Maremare whānau, including people with diverse experiences of relationships with Turakina, with critical reflection on relevant concepts from Te Ao Māori. Specifically, mobilising a Kaupapa Māori Theory approach, the thesis draws on an oriori, ‘He Oriori mō Wharaurangi’ as the methodology and foundation of this thesis. In this way, the thesis both argues and demonstrates the value of researchers, and in particular kairangahau Māori, look to mātauranga Māori to guide us in our research about Māori topics. This oriori is iwi specific, it holds kōrero tuku iho that pertains to my whakapapa as an uri of Ngā Wairiki Ngāti Apa and it is very relevant to both my kaupapa and the people participating in the rangahau.

Ultimately, whakapapa rests at the core of connection to whenua tupuna. It is the foundation in which we stand and it connects us as a people to the land that birthed us. This thesis looks at the different ways that our tūpuna call us home, the different ‘returning home’ experiences within the Maremare whānau and how people within the whānau view their connection with our whenua tupuna in Turakina. Nā wai koe? Our tūpuna call us home.

year: 2021