Second Set Of Variables Research Articles

Supervised Learning Algorithm for Predicting Mortality Risk in Older Adults Using Cardiovascular Health Study Dataset

Multiple chronic conditions are an important factor influencing mortality in older adults. At the same time, cardiovascular events in older adult patients are one of the leading causes of mortality worldwide. This study aimed to design a machine learning model capable of predicting mortality risk in older adult patients with cardiovascular pathologies and multiple chronic diseases using the Cardiovascular Health Study database. The methodology for algorithm design included (i) database analysis, (ii) variable selection, (iii) feature matrix creation and data preprocessing, (iv) model training, and (v) performance analysis. The analysis and variable selection were performed through previous knowledge, correlation, and histograms to visualize the data distribution. The machine learning models selected were random forest, support vector machine, and logistic regression. The models were trained using two sets of variables. First, eight years of the data were summarized as the mode of all years per patient for each variable (123 variables). The second set of variables was obtained from the mode every three years (369 variables). The results show that the random forest trained with the second set of variables has the best performance (89% accuracy), which is better than other reported results in the literature.

Predictors affecting personal digital information management activities: A hierarchical regression analysis

The study aimed to investigate the influence of demographic characteristics, Internet use, computer knowledge and technology self-efficacy on personal digital information management (PIM) activities – information finding, information keeping, information organising and information re-finding. The design of the study was quantitative and a survey method was used to get the objectives of the study. Three independent institutes of art and design – the Institute of Art and Culture, the National College of Arts and Design and the Pakistan Institute of Fashion and Design – were chosen as the research setting of the study. The population of the study was an academic community of three art and design institutes. The questionnaire was distributed to faculty and students of respective institutes and 229 responses were received after follow-up. The findings of the study indicated that both demographic characteristics and technology-related factors influenced the arts and design academic community’s PIM activities. However, the second set of variables – Internet use, computer knowledge and technology self-efficacy influenced more than demographic variables on PIM activities. Academic role, university and technology self-efficacy appeared significant predictors of all PIM activities. The findings might be helpful for arts and design institutes librarians to make strategies to improve academic community’s personal information management skills. In arts and design institutes, better efficiency in faculty and students’ PIM could be achieved if PIM literacy programmes are designed paying attention to differentials in demographic factors and technology-related factors, as revealed in this study.

Environmental and biological factors affecting the abundance of Prosopis flexuosa saplings in the central-west Monte of Argentina

Trees and forests in drylands help mitigate the challenges through provision of economic products and vital environmental services such as habitat for biodiversity, prevention of erosion and desertification, regulation of water, microclimate, and soil fertility. The condition and changes in dry forests can be assessed by using ecological indicators able to quantify spatial and temporal changes in vegetation. One of the ways to determine the condition of the forest is to study the dominant tree species and its regeneration. Our study aimed to assess whether the abundance of Prosopis flexuosa saplings is affected by environmental and biological factors. To evaluate the first variables we used data from remote sensing such as satellite images and Aster Global Digital Model (GDEM). The second set of variables was about exotic and native ungulates and we used feces of these animals and camera traps to take data. We found that sapling abundance related positively to sandy substrates and negatively to Wetness Index. On the other hand, in relation to biological variables, the abundance of saplings was positively affected by density of adult trees and by number of seeds dispersed by equines, but space use by Lama guanicoe had a negative relationship with saplings. This research shows that P. flexuosa saplings are benefited from sandy substrates and the conditions around adult trees. In addition to this, we found that exotic ungulates in low densities have neutral (i.e. cattle) or positive (i.e. equines) effects on sapling abundance. Based on these findings, we conclude that regeneration of the population of P. flexuosa in our study area has no major problems. In addition, we corroborated that the presence of exotic and domestic ungulates in low densities does not have deleterious consequences for saplings of the dominant tree, P. flexuosa .

Information asymmetry among dairy producers in Paraná, Brazil

Milk production has great social and economic importance in Paraná, Brazil. However, dairy farmers have abandoned the activity over the past few years because of difficulties in meeting institutional and market demands for increased milk production and quality. Information asymmetry between dairy farmers and market agents may be contributing to this scenario. It occurs when one agent in a transaction has more or better information than another. Information asymmetry can encourage opportunistic behavior and negatively affect the relationship between parties. These problems can be minimized or resolved by horizontal collaboration, such as participation in farmers’ organizations, cooperatives, or associations. The aim of this study was to assess the extent of information asymmetry among dairy farmers and investigate whether participation in farmers’ organizations strengthens buyer–seller relationships and stimulates compliance with milk quality standards. A total of 204 semi-structured questionnaires were applied to head farmers of dairy production systems in Paraná. Two sets of variables were analyzed: variables related to socio-economic and production characteristics and variables related to transactions between farmers and the dairy industry and the head farmer’s knowledge about milk quality regulations. The second set of variables was subjected to common factor analysis, which generated four factors: F1, knowledge about institutional requirements; F2, technical support from the buyer; F3, technical knowledge; and F4, level of trust in the buyer. Dairy farmers who did not participate in farmers’ organizations operated under greater information asymmetry and were disadvantaged with regard to F2, F3, and F4 (P < 0.05). Participation in horizontal collaborations can help farmers survive and thrive in the dairy activity.

Information asymmetry among dairy producers in Paraná, Brazil

Milk production has great social and economic importance in Paraná, Brazil. However, dairy farmers have abandoned the activity over the past few years because of difficulties in meeting institutional and market demands for increased milk production and quality. Information asymmetry between dairy farmers and market agents may be contributing to this scenario. It occurs when one agent in a transaction has more or better information than another. Information asymmetry can encourage opportunistic behavior and negatively affect the relationship between parties. These problems can be minimized or resolved by horizontal collaboration, such as participation in farmers’ organizations, cooperatives, or associations. The aim of this study was to assess the extent of information asymmetry among dairy farmers and investigate whether participation in farmers’ organizations strengthens buyer–seller relationships and stimulates compliance with milk quality standards. A total of 204 semi-structured questionnaires were applied to head farmers of dairy production systems in Paraná. Two sets of variables were analyzed: variables related to socio-economic and production characteristics and variables related to transactions between farmers and the dairy industry and the head farmer’s knowledge about milk quality regulations. The second set of variables was subjected to common factor analysis, which generated four factors: F1, knowledge about institutional requirements; F2, technical support from the buyer; F3, technical knowledge; and F4, level of trust in the buyer. Dairy farmers who did not participate in farmers’ organizations operated under greater information asymmetry and were disadvantaged with regard to F2, F3, and F4 (P < 0.05). Participation in horizontal collaborations can help farmers survive and thrive in the dairy activity.

High-Dimensional Sparse Financial Networks Through a Regularised Regression Model

We propose a shrinkage and selection methodology specifically designed for network inference using high dimensional data through a regularised linear regression model with Spike-and-Slab prior on the parameters. The approach extends the case where the error terms are heteroscedastic, by adding an ARCH-type equation through an approximate Expectation-Maximisation algorithm. The proposed model accounts for two sets of covariates. The first set contains predetermined variables which are not penalised in the model (i.e., the autoregressive component and common factors) while the second set of variables contains all the (lagged) financial institutions in the system, included with a given probability. The financial linkages are expressed in terms of inclusion probabilities resulting in a weighted directed network where the adjacency matrix is built “row by row. In the empirical application, we estimate the network over time using a rolling window approach on 1248 world financial firms (banks, insurances, brokers and other financial services) both active and dead from 29 December 2000 to 6 October 2017 at a weekly frequency. Findings show that over time the shape of the out degree distribution exhibits the typical behavior of financial stress indicators and represents a significant predictor of market returns at the first lag (one week) and the fourth lag (one month).

The Factors Effect on Weaning Weight of Hair Goats in West Anatolia

Objective:In this study; the relationships of several traits effects of Turkish Hair Goats were investigated by canonic correlation analysis. Therefore determining more effective factors on weaning weight of Hair goats are aimed.Material and Methods: The data were utilized from 1020 male and 1011 female Hair goat kids which were born in the period 2013 to 2016. The herd, year of birth, birth type and sex are analyzed as the first set of variables (X); weight at birth, weight at thirty day of age and weight at weaning age effects are analyzed as the second set of variables (Y). By performing canonical correlation analysis, canonical correlations between the first, second and third pair of canonical variates were estimated and three canonical correlation were found significant (P < 0.01).Results:The biggest correlations between year of birth, sex, birth type variables and v1, v2 and v3 canonical variates were found -0.77%, 0.87% and -0.82%, respectively. The biggest correlations between weight at birth, weight at thirty day of age and weight at weaning age and w1, w2 and w3 canonical variates were found 0.84%, -0.97% and -0.78%, respectively. As a result it has been determined that sex and birth type play an important role in the formation of the goat kid’s body weights.Conclusion:Hair goats reared in West Anatolia are slaughtered mostly at the end of the weaning. Because of this, assessment the factors effect on weaning weight are important. Canonical correlation analysis therefore is used to analyze all factors at the same time to define relationships between each other of weaning traits for hair goats.

Explaining military coups and impeachments in Latin America

We develop a unified theory of presidential instability to explain why presidents are removed from office through military coups or through legal procedures. While some causal factors motivate opponents to overthrow the president irrespective of the specific mechanism employed, other factors expand the relative capabilities of groups inclined to pursue military or civilian action. The first group of variables, including economic recession, protests, and radicalization, explains why presidents fall. The second set of variables, including regional diffusion, partisan support for the executive, and normative support for democracy, explains how they are ousted. We test this theory using discrete-time event history models with sample selection on a novel database for 19 Latin American countries between 1945 and 2010.

A multivariate approach for the study of environmental drivers of wine economic structure

Vitivinicultural ⿿terroir⿿ is a concept referring to an area in which the collective knowledge of the interactions between environment and vitivinicultural practices develops, providing distinctive characteristics to the products. The effect of the environmental components over the terroir wines has been already widely demonstrated, but their possible effect on the overall system of production and organization of the viticultural farms and wine industry, called ⿿wine economic structure⿿ in this study (WES), is still unknown. Thus the research hypothesis of this work was that there are environmental drivers influencing WES.The investigation was carried out at the national scale, taking Italy as study area, the biggest wine producer of the world. We used viticultural territories singled out at 1:500,000 reference scale, called viticultural Macroareas, which grouped Denomination of Origin areas with respect of geographic proximity, environmental features, viticultural affinity, and wine tradition. The characterization of WES was based on the official data reported in the wine production declarations related to the year 2008. Statistics were taken into account about general quantitative variables of wine farms, presence of associative forms, degree of vertical integration of wineries, quality orientation of wine producers, and acreage of vineyard. Environmental variables related to climate, soil, and vegetation vigour were selected for their direct influence on the vine growing. A second set of variables was chosen to express the effect of land topography on viticultural management. A third one was intended to discover the possible relationships between WESs and land qualities, such as the indexes of sensitivity to desertification, the soil resistance to water erosion, and land vulnerability. A multivariate approach was carried out to elaborate the many studied variables and their interactions. Nonparametric statistics were also used, to overcome the necessity of normal distributions. A PCA was carried out separately for the environmental and economic data, to reduce the dimension of datasets. The new economic and environmental synthetic descriptors were submitted to three multivariate analyses: (i) the correlation between economic and environmental descriptors, through the non-parametric Spearman test; (ii) a cluster analysis to group the Macroareas in few homogeneous WESs; (iii) a discriminant analysis of economic clusters and environmental factors, to highlight the environmental drivers of WESs. The results highlighted that in some Macroareas there is a direct correlation between high quality wine production and presence of low available soil water, due to both soil and climatic limitations. In other Macroareas, the limited quantity and quality of wine yield showed a significant relationship with the presence of rough morphologies and high environmental variability. Similarly, heterogeneous soil and morphological conditions fitted well with both the absence of well-defined economic structures, and a relatively lower quality production. The cluster analysis identified six main WESs for the whole country. Climatic, pedoclimatic, morphological mean conditions and topographic heterogeneity of Macroareas had the most important discriminant power over the clusters. The result of this multivariate analyses proved that the different WESs of a country are significantly influenced by specific landscape characteristics. Therefore, landscape care has a strategic role also on the development of the wine industry.

Patterns of variation and correlation in quality control characteristics of segment installation

Purpose – Evaluation of the quality and performance of a tunnel lining during the installation of segments are the main objects of tunneling projects. Because the quality is affected by several attributes, the purpose of this paper is an appropriate multivariate data analysis that is helpful in extracting applicable knowledge of the data collected regarding the related attributes of the initial installed rings. Design/methodology/approach – Principal component analysis (PCA) is used to analyze the data obtained by the quality control team. The authors use canonical correlation analysis (CCA) to extract some linear combinations of the original attributes of the two groups that produce the largest correlations with the second set of variables. Findings – The authors reduce the dimensionality of the original data set for further analyses, and use a small number of uncorrelated variables rather than a larger set of correlated variables to take effective and efficient action to control the quality of the tunnel lining. The authors also explore the correlation structure and relationship between two main groups of characteristics used for assessing the quality of the installed rings. Then, instead of a large number of the original characteristics in the two groups, the authors can easily control these few to attain a reasonable quality for the tunnel lining. Originality/value – This is a case study, and for each ring selected for inspection, 16 different characteristics are measured and the observations are recorded. The authors use PCA and CCA to analyse the data and interpret the results. Although the methods are not new, applying them to this data results in useful and informative outcomes and interpretation.

ÖĞRENCİLERİN MEMNUNİYET DÜZEYLERİ İLE DEMOGRAFİK ÖZELLİKLERİ ARASINDAKİ İLİŞKİNİN DOĞRUSAL OLMAYAN KANONİK KORELASYON ANALİZİ İLE İNCELENMESİ: MESLEK YÜKSEKOKULU'NDA BİR UYGULAMA

In this study, the relationship between students’ demographic characteristics and students’ satisfaction levels were examined by the non-linear canonical correlation analysis that used to examine the relationship between the categorical two sets of variables. The data were obtained from questionnaires applied to 973 vocational high school students. The relationship between first set of variables that consist of students’ satisfaction towards consultancy, the management of department and university, lecture and syllabus, physical facilities and teaching staff and second set of variables that consist of students’ demographic characteristics was examined. The relationship between these two sets of variables was determined not very strong which is 0.272 for first dimension and 0.260 for second dimension. According to the findings of the study, the separation power of the physical facilities is high and were found to be the main variable with the highest load value

Modeling the Inference between Upstream Inflow Hydrographs and Downstream Flooded Areas in a Reservoir Driven System

Management of a river imposes a task for several institutional levels in every part of the world, especially when reservoirs are the main flow drivers. The research presented herein shows how management of the flow in a reservoir driven system can be modeled by using an inference simulation model between two sets of variables, as an alternative to an approach based on an optimization model. The first selected variables are the explanatory ones, which refers to the relation between the operation of reservoirs and the input hydrographs to each of the considered reservoirs. The second set of variables are descriptive and refers to the resulting flooding hydrograph at a hydrological station located downstream of the reservoir network. The Yellow River, in China, is chosen to demonstrate the concept, however the methodology can be applied in practice for any reservoir driven system. In the mid stream of the Yellow River, a system of four reservoirs was built to manage flooding, using daily information and taking daily decisions. The proposed method uses a simplified simulation model of the actual reservoir operation to determine a multiple linear regression model between the set of explanatory and descriptive variables. The set of explanatory variables is very large and the demonstration is done on a selected subset of variables. Each selection of an explanatory variable is based on a correlation analysis with respect to the original set. Analysis of the model results shows that the reduction of the number of variables does not decrease the model fitness and robustness.

Spatial and temporal dynamics of biotic and abiotic features of temperate coastal ecosystems as revealed by a combination of ecological indicators

Coastal ecosystems exhibit complex spatio-temporal patterns due to their position at the interface between land and sea. This is particularly the case of temperate ecosystems where exploitation of coastal resources (fisheries and aquaculture) and intensive agricultural use of watersheds further complicate our understanding of their dynamics. The aim of the present study was to unravel the spatio-temporal dynamics of contrasted megatidal coastal ecosystems located at the same regional scale (i.e. under the same regional climate), but under different kinds of human pressure. Two kinds of ecological indicators were assessed over a period of four years at 11 locations along the coast of the Cotentin peninsula (Normandy, France). A first set of hydrobiological variables (dissolved nutrients, Chl a, temperature, salinity, etc.) was measured fortnightly in the water column. These data were analysed by principal components analysis (PCA). A second set of variables were the carbon and nitrogen stable isotope ratios of the adductor muscles of cultured Crassostrea gigas introduced every year to typify the bentho-pelagic coupling at each location. Food sources were also investigated using a mixing model with data on the isotopic composition of the food sources obtained previously. To identify which environmental variables played a significant role in determining the oyster diet, the contributions of oyster food sources were combined with environmental variables in a canonical correspondence analysis (CCA). Isotopic values of adductor muscles varied significantly between −20.12‰ and −16.79‰ for δ13C and between 8.28‰ and 11.87‰ for δ15N. The PCA distinguished two groups of coastal ecosystems that differed in their coastal hydrology, nutrient inputs, and the size of their respective watershed, irrespective of the year. In each zone, different spatial patterns in the measured variables were observed depending on the year showing that local impacts differed temporally. As revealed by CCA, food sources used by the oysters were mainly explained by salinity suggesting regional differences between ecosystems. On the west coast of the peninsula, climatic factors act in synergy with anthropogenic factors (i.e. nutrient enrichment) whereas on the east coast, climatic factors appear to be dampened by anthropogenic factors.

Urine Flow Is a Novel Hemodynamic Monitoring Tool for the Detection of Hypovolemia

Noticeable changes in vital signs indicating hypovolemia occur only after 15% of the blood volume is lost. More sensitive variables (e.g., cardiac output, systolic pressure variation and its Δdown component) are invasive and difficult to obtain in the early phase of bleeding. Lately, a new technology for continuous optical measurements of minute-to-minute urine flow rates has become available. We performed a preliminary evaluation to determine whether urine flow can act as an early and sensitive warning of hypovolemia. Eleven patients (ASA physical status I-II) undergoing posterior spine fusion surgery were studied prospectively. Study variables included heart rate, blood pressure (systolic and diastolic), systolic pressure variation and Δdown, minute urinary flow, hemoglobin, blood and urinary sodium, and creatinine in the blood and urine. Urine flow rate was measured using URINFO 2000™ (FlowSense Medical, Misgav, Israel). After recording baseline variables, 10 mL/kg of the patient's blood was shed and a second set of variables was recorded. Subsequently, hypovolemia was reversed by infusing colloid solution (hetastarch 6%) followed by recording a third set of variables. These 3 observations were then compared. An average of 614 ± 143 mL (mean ± SD) of blood was shed. During phlebotomy, the mean urine flow rate decreased from 5.7 ± 8 mL/min to 1.07 ± 2.5 mL/min. Systolic blood pressure and hemoglobin also decreased. Δdown increased. After rehydration, urine flow, blood pressure, and Δdown values returned to baseline. The hemoglobin concentration decreased whereas other variables did not change significantly. Urine flow rate is a dynamic variable that seems to be a reliable indicator of changes in blood volume. These results justify further investigation.

Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of soil data

Soil textural components are the most common inputs in pedotransfer functions (PTFs), although other soil properties, such as bulk density, organic matter content and structural indexes, are also used. The possibility of using the geometric mean d g and the geometric standard deviation σ g of soil particle diameters instead of soil particle size distribution to derive PTFs was investigated. Soil samples (234) having different particle size distributions were collected randomly. The predictor variables were separated into two groups: (i) particle-size distribution and bulk density, (ii) bulk density, geometric mean and geometric standard deviation of particle diameters. Two point PTF types and two parametric PTF forms were developed to predict six points on the retention curve and the parameters of the van Genuchten model, using the stepwise regression method. The derived PTFs were compared with the Rosetta package. The results indicated that the second set of variables accounted for 65 and 90% of the variation in α and 0 s parameters of the van Genuchten, model respectively. The results also indicated that these variables better explained the variation of the n parameter (the shape parameter of the van Genuchten model) and the water contents at -30, -100, -300, -500 and -1500 kPa matric potentials. It was concluded that, at the drier end of the retention curve, d g and σ g predict the water contents better than the soil bulk density. Although the coefficients of determination of the derived PTFs with the second set of variables were higher than that of the first group, the first set of variables gave more reliable estimates. Results showed that the descriptors of the particle size distribution (d g and σ g ) did not improve the quality of soil water retention prediction. The reliability test indicated that the derived PTFs predict soil hydraulic properties better than the Rosetta package.

Professional Overcharging in Large Bankruptcy Reorganization Cases

In an empirical study of professional fees and expenses in 74 large public company bankruptcies concluded 1998–2003, we found that (1) controlling for the trend over time and the geographical location of the cases, company size (measured by assets), case duration (measured in days), and the number of parties (measured by the number of professional firms working) explain nearly 87 percent of the case‐to‐case variation in professional fees, (2) fees and expenses increased about 10.4 percent per year over the six‐year period covered by our study, (3) five of six predictors of fees and expenses exhibited a strong scale effect, (4) the scale effect for company size is so severe that reporting fees as a simple percentage of assets is misleading, (5) using the same model we used with court file data, our variables explain 86 percent of the case‐to‐case variance in the amounts of professional fees and expenses reported in SEC filing data, and (6) fees and expenses reported in SEC filing data are highly correlated with those reported in court file data, but are 58 percent higher. The principal determinants of fees and expenses—assets, days in bankruptcy, and the number of professional firms working—appear to us to measure not only the need for professional services, but also the opportunity for professionals to bill. In an attempt to statistically isolate this “billing opportunity” component of fees and expenses, we compiled a second set of variables—employees, docket length, and reorganization plan classes—that we believe measures case complexity without measuring billing opportunity. When those variables are substituted for the principal determinants, the regression explains substantially the same percentage of variance in fees and expenses. This second, complexity‐only model predicts fees that, controlling for scale, are significantly lower for companies with assets greater than about $770 million. We theorize that this systematic difference in the two models' predictions measures the billing opportunity component of fees and expenses in large public company bankruptcies. That component is an overcharge because it is not warranted by the complexity of the cases, including complexity resulting from company size.

The relationship between environmental variation and species abundance in shrimp community (Crustacea: Decapoda: Penaeoidea) in south-eastern Brazil

The impact of shrimp fisheries in tropical regions has become comparable to the world's most intensively exploited temperate shelf ecosystems. The increase in the fishing fleet in south-eastern Brazil and the decrease in landings of profitable shrimp species have contributed to the incorporation of additional species into those fisheries. The goal of the present study is to investigate the influence of environmental factors on the abundance patterns of shrimp communities on the south-eastern coast of Brazil, over a period of two years. Monthly collections were conducted in the Ubatuba and Caraguatatuba regions using a commercial shrimp fishing boat equipped with ‘double-rig’ nets. Each region was divided into 7 sampling stations up to 35 m deep. The relationship between the environmental factors and the abundance patterns in the shrimp communities was assessed using a canonical correlation analysis (CCorrA). The first set of variables used during the CCorrA included environmental characteristics and the second set of variables the abundance of the studied species. A total of 374,915 individuals were collected during the present study. Xiphopenaeus kroyeri showed the highest abundance (273,127), followed by Artemesia longinaris (73,422), and Pleoticus muelleri (15,262). In the first root, depth and temperature showed the highest factor loadings (0.9 and −0.7) and canonical weights (0.6 and −0.4). These environmental factors were strongly associated with the abundance of X. kroyeri (factor loading =−0.9 and canonical weight =−0.9). The second root demonstrated a positive relationship between abundance of P. muelleri and depth, and an inverse association with bottom temperature. The abundance patterns of X. kroyeri and P. muelleri were strongly affected by the water mass South Atlantic Central Water (cold waters =15°C), which can lead to a temperature decrease in deeper areas (>15 m). Thus, the opposite abundance trend for depth of these species might reflect bathymetric variation in temperature, a clear example of distinct behavioural differences of species of different origins, either tropical (X. kroyeri) or subantarctic (P. muelleri). The low overall association between environmental parameters and shrimp abundance patterns indicates that each studied species might have responded idiosyncratically to environmental variation, such that a general community-level response was not apparent. However, other confounding factors such as intraspecific migration patterns might have also played a role in generating the observed patterns.

Destination Evaluation: The Role of Previous Travel Experience and Trip Characteristics

This study uses canonical correlation analysis to examine the relationship between two sets of variables that can be used in evaluating a travel destination. The first set is composed of five variables that are referred to as previous travel experience and trip characteristics. These five variables include two measures of previous travel experience (number of countries visited, number of countries visited for pleasure) and three trip characteristics (length of stay, number of people in the travel party, and travel mode). The second set of variables consists of four criterion variables referred to as destination evaluation variables. These variables (satisfaction, service quality, value, and likelihood to return) are used to evaluate the travel destination. Results of the study indicated that a portion of the variance in destination evaluation can be attributed to previous travel experience and trip characteristics.

Intensification and diversification of New Zealand agriculture since 1960: An evaluation of current indicators of land use change

Previous studies of New Zealand's environmental and agricultural history have provided a broad-brush characterisation of land use change that potentially misses pivotal fluctuations in land use policy and practice that would inform us of key drivers of ongoing agricultural land use change. Of particular interest to policy makers is the period after the end of agriculture's 'long boom' in the late 1970s, when a dramatic change in economic policy occurred and farming subsidies were removed. A review and principal components analysis of 35 New Zealand agricultural statistics from the past 40 years identified two main patterns of change in land use, production, and farm inputs. One set of variables, which explained 49% of the variation, indicates an overarching, strong and steady trend for agricultural intensification and to a lesser extent diversification, as indicated by (a) increasing stocking rates and yields, (b) increased farm fertiliser, pesticide and food stock inputs, (c) conversion to more intensive forms of agriculture, and (d) diversification into forestry and deer farming. A second group of variables, which explained 22% of overall variation, inflects around 1982/1983, the time of a major shift in agri-economic policy that removed farm subsidies. The second group of changes included some contraction in agriculture (especially in sheep farming) and its associated inputs and a decline in rural population. There is evidence of acceleration in intensification and diversification in the past decade and for slowing in the contraction of the second set of variables between 1997 and 2001. The drivers of these changes are poorly understood and their impacts on biodiversity conservation in farmed landscapes cannot be discerned from the national indicators currently being monitored. The accelerating agricultural intensification over the past 40 years raises concern about whether New Zealand farming is broadly ecologically sustainable now, and especially whether it could remain so in future.

Canonical correlation analysis for studying the relationship between egg production traits and body weight, egg weight and age at sexual maturity in layers

In this study, canonical correlation analysis was applied to layer data to estimate the relationships of egg production with age at sexual maturity, body weight and egg weight. For this purpose, it was designed to evaluate the relationship between two sets of variables of laying hens: egg numbers at three different periods as the first set of variables (Y) and age at sexual maturity, body weight, egg weight as the second set of variables (X) by using canonical correlation analysis. Estimated canonical correlations between the first and the second pair of canonical variates were significant (P < 0.01). Canonical weights and loadings from canonical correlation analysis indicated that age at sexual maturity had the largest contribution as compared with body weight and egg weight to variation of the number of egg productions at three different periods.