Assessing local knowledge use in agroforestry management with cognitive maps

Small-holder farmers often develop adaptable agroforestry management techniques to improve and diversify crop production. In the cocoa growing region of Ghana, local knowledge on such farm management holds a noteworthy role in the overall farm development. The documentation and analysis of such knowledge use in cocoa agroforests may afford an applicable framework to determine mechanisms driving farmer preference and indicators in farm management. This study employed 12 in-depth farmer interviews regarding variables in farm management as a unit of analysis and utilized cognitive mapping as a qualitative method of analysis. Our objectives were (1) to illustrate and describe agroforestry management variables and associated farm practices, (2) to determine the scope of decision making of individual farmers, and (3) to investigate the suitability of cognitive mapping as a tool for assessing local knowledge use. Results from the cognitive maps revealed an average of 16 ± 3 variables and 19 ± 3 links between management variables in the farmer cognitive maps. Farmer use of advantageous ecological processes was highly central to farm management (48% of all variables), particularly manipulation of organic matter, shade and food crop establishment, and maintenance of a tree stratum as the most common, highly linked variables. Over 85% of variables included bidirectional arrows, interpreted as farm management practices dominated by controllable factors, insofar as farmers indicated an ability to alter most farm characteristics. Local knowledge use on cocoa production revealed detailed indicators for site evaluation, thus affecting farm preparation and management. Our findings suggest that amid multisourced information under conditions of uncertainty, strategies for adaptable agroforestry management should integrate existing and localized management frameworks and that cognitive mapping provides a tool-based approach to advance such a management support system.     

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Introduction

The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study.

Method

Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it.

Results

The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals.

Impact

The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.     

Prioritizing the provision of urban ecosystem services in deprived areas,a question of environmental justice

The distribution of urban ecosystem services (UES) is often uneven across socioeconomic groups, leading to environmental justice issues. Understanding the distribution of UES across a landscape can help managers ensure an equitable distribution of services. While many past studies have focused on the distribution of green spaces in relation to socioeconomic variables, this research analyzes the distribution of UES provided by these green spaces. This research quantified air pollution removal, atmospheric carbon reduction, and surface runoff mitigation provided by urban trees in Strasbourg city (France). The provision of these three UES was studied at the census block scale by creating an index of UES delivery, which was contrasted with a constructed social deprivation index. Our results show that there is no significant association between the delivery of UES and social deprivation. Some deprived populations benefit from high UES delivery. Results also suggest that mapping associations between UES delivery and social deprivation should be integrated with future development plans to enhance the equitable distribution of UES. This study provides insights into the French context where studies about the distribution of UES at a small-area level remain lacking.     

Biodiversity conservation through faith and tradition in India: some case studies

SUMMARY

In India, since ancient times, several members of the flora and fauna have been identified with particular personalities of the Hindu pantheon and are worshipped. Many wild animals, such as tigers, lions and elephants, and birds such as the peacock, owl and swan have been revered as the vehicles of Gods and Goddesses and worshipped. Several trees, such as mango, coconut, khejri, emblica, tamarind, ashoka, madhuca and wood apple, and herbs such as basil are considered highly sacred and worshipped. This belief and these practices continue in one form or another even today in certain sections of Indian society, particularly the rural folk and the aboriginal tribes living in the forest. Nature worship is a form of tribal belief and the faith of these laymen has helped to conserve many natural ecosystems in India. They have preserved many pristine forests—called ‘sacred groves’ in their original forms. Sacred groves are rich in biodiversity. They are the last refuge of the rare flora and fauna which have otherwise disappeared from the modern world. Sacred groves have become part of the ‘biosphere reserves’ of India.     

鄱阳湖地区洪水灾害模式分析

在对多年水文数据进行分析的基础上，利用Kohonen自组织映射对鄱阳湖地区降水时序变化、长江来水和湖水水位变化进行模式识别。之后，将所得的模式作为解释变量，用CART方法对样本年份进行分类，建立了鄱阳湖区灾害模式判别树。借此揭示了鄱阳湖地区降水和长江来水对鄱阳湖洪涝灾害的影响。     

Effects of sample survey design on the accuracy of classification tree models in species distribution models

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys.     

Learning to predict channel stability using biogeomorphic features

Current human land use activities are altering many components of the river landscape, resulting in unstable channels. Instability may have serious negative consequences for water quality, aquatic and riparian habitat, and for river-related human infrastructure such as bridges and roads. Resource management agencies have developed rapid bioassessment surveys to help assess stability in a fast and cost-effective way. While this assessment can be done for a single site fairly rapidly, it is still time-consuming to apply over large watersheds and assessment activities must be prioritized. We constructed a system that employs commonly available map data as inputs to cost-sensitive variants of decision tree algorithms to predict the relative channel stability of different sites. In particular, we use bagged lazy option trees (LOTs) and bagged probability estimation trees (PETs) to identify all unstable channels while making the smallest number of errors of classifying stable channels as unstable, thereby minimizing cost and maximizing safety. We measured the performance of the classifiers using ROC curves and found that the PETs performed better than the LOTs in situations where the number of instances of the stable and unstable classes were relatively balanced, but the LOTs did better where unstable examples were relatively rare compared to stable, perhaps due to the LOTs’ ability to focus on individual examples.     

A flood risk decision making approach for Mediterranean tree crops using GIS; climate change effects and flood-tolerant species

The aim of this study is to estimate flood risk in the Mediterranean island of Crete in Greece, using Geographic Information Systems (GIS). The island of Crete, covering an area of 8265 km², is one of the most intensively Mediterranean agricultural areas dominated by fruit tree crops. In this study, the factors that are directly related to the creation of a flood are combined in a GIS environment in order to identify the most prone flooding areas. These factors are: (a) the Flow accumulation (F), (b) the Rainfall intensity (R), (c) the Elevation (E), (d) the Geology (G), (e) the Land use (L), and (f) the Slope (S). The initials of these factors gave the name to the proposed method: “FREGLS”. The above factors are presented in the form of grid maps and are used in order to determine the final flood risk map. Subsequently, the flood prone fruit tree areas of Crete can be estimated by applying a methodology based on weighting coefficients. The reliability of the final flood risk map is verified using historical flooded data. Additionally, the impact of global climate change scenarios A2 and B1, on flood risk in Crete is examined. Based on the above, this study highlights the flood prone fruit tree areas in the island of Crete under current and future climate conditions. Also, flood-tolerant fruit trees which appear to be economically important for Greece are recommended, especially for the high flood risk areas of the island. The proposed methodology can be applied as a decision making tool for flood risk mitigation to any river basin where tree crops are cultivated.     

Does defoliation induce chemical and morphological defenses in the leaves of silver birch seedlings under changing climate?

Summary.  We examined the effects of defoliation con-currently with elevated temperature and CO₂ on some chemical and morphological characteristics in the leaves of silver birch seedlings (Betula pendula). We also analyzed the consequent changes in the palatability of leaves for adult blue alder leaf beetles (Agelastica alni). Under the different climatic treatments, the seedlings were subjected to three fertilizer treatments (0 kg, 130 kg and 270 kg N ha⁻¹) and defoliation treatments (0%, 25% and 50% of the total leaf area). In each climatic treatment, fertilization increased the nitrogen content in the leaves, but decreased total concentrations of soluble phenolics, detected by high-performance liquid chromatography (HPLC), and insoluble condensed tannins. Defoliation, both independently and in combination with elevated temperature and CO₂, decreased the concentrations of the phenolics. Compared to the intact controls, the leaves of the defoliated seedlings were smaller and tougher. Under elevated temperature, the beetles consumed a smaller amount of the leaves of plants subjected to the high fertilization, while under ambient climatic conditions, fertilization increased the feeding. The total leaf consumption was higher under the ambient climatic conditions than under elevated temperature, elevated CO₂ or the combination of elevated temperature and CO₂.     

桂西北果树业的结构优化及其产业化发展——以广西自治区环江县为例

广西壮族自治区环江县选择果树业作为科技扶贫的支柱产业是近几年来探索出来的一条成功经验。通过对环江县科环公司果树业发展模式的分析,作者对桂西北果树业的结构优化及产业化道路进行了探讨。