Bioeconomic meta-modelling of Indonesian agroforests as carbon sinks

In many areas of developing countries, economic and institutional factors often combine to give farmers incentives to clear forests and repeatedly plant food crops without sufficiently replenishing the soils. These activities lead to large-scale land degradation and contribute to global warming through the release of greenhouse gases into the atmosphere. We investigate whether agroforestry systems might alleviate these trends when carbon-credit payments are available under the Clean Development Mechanism of the Kyoto Protocol. A meta-modelling framework is adopted, comprising an econometric-production model of a smallholding in Sumatra. The model is used within a dynamic-programming algorithm to determine optimal combinations of tree/crop area, tree-rotation length, and firewood harvest. Results show the influence of soil-carbon stocks and discount rates on optimal strategies and reveal interesting implications for joint management of agriculture and carbon.bio-economic meta-modelling, Indonesia, agroforestry, carbon credits, dynamic programming, Environmental Economics and Policy, Resource /Energy Economics and Policy,

Optimal Land-Use Decisions in the Presence of Carbon Payments and Fertilizer Subsidies: An Indonesian Case Study

The Clean Development Mechanism of the Kyoto Protocol provides the opportunity for smallholders to receive financial rewards for adopting tree-based systems that are sustainable. In this paper a meta-model is developed to simulate interactions between trees, crops and soils under a range of management regimes for a smallholding in Sumatra. The model is used within a dynamic-programming algorithm to determine optimal tree/crop areas, tree-rotation lengths, firewood-harvest and fertilizer application rates for a landholder faced with deteriorating land quality and opportunities to receive carbon credits and fertiliser subsidies. It is found th at profit maximising management strategies depend on initial soil quality. For example, incentives to participate in carbon projects only exist when the soil is degraded because the opportunity cost of the forgone crop production is low. Also, when soil-carbon stocks are low only trees should be grown and residues added to the soil to increase carbon stocks until a threshold level is reached, when it becomes optimal to switch to a steady-state system of crops with fertiliser. In this case, tree rotation lengths depend on carbon and fertiliser prices; where increases in these prices decrease the opportunity cost of growing trees and allow for longer rotations. If, however, the initial soil-carbon stock is high, the profit-maximising strategy is to grow only crops and use fertiliser, which initially depletes the soil of carbon until a steady state is reached and maintained.Land Economics/Use,

Tree-crop interactions and their environmental and economic implications in the presence of carbon-sequestration payments

Growing trees with crops has environmental and economic implications. Trees can help prevent land degradation and increase biodiversity while at the same time allow for the continued use of the land to produce agricultural crops. In fact, growing trees alongside crops is known to improve both the productivity and sustainability of the land. However, due to high labour-input requirements, high costs of establishment, and delayed revenue returns, trees are often not economically attractive to landholders. Because of the Kyoto Protocol, and the growing emphasis on market-based solutions to environmental problems, the ability of trees to sequester and store CO2 has altered the economic landscape of agroforestry systems. The economic and management implications of carbon-sequestration payments on agroforestry systems are addressed in this study using a bioeconomic modelling approach. An agroforestry system in Indonesia is simulated using a biophysical process model. A general economic analysis of this system, from the standpoint of individual landholders, is then developed and the implications for management and policy are discussed.agroforestry, bioeconomics, tree/crop interactions, carbon credits, baselines, Environmental Economics and Policy, Land Economics/Use,

Carbon-accounting methods and reforestation incentives

The emission of greenhouse gases, particularly carbon dioxide, and the consequent potential for climate change are the focus of increasing international concern. Temporary land-use change and forestry projects (LUCF) can be implemented to offset permanent emissions of carbon dioxide from the energy sector. Several approaches to accounting for carbon sequestration in LUCF projects have been proposed. In the present paper, the economic implications of adopting four of these approaches are evaluated in a normative context. The analysis is based on simulation of Australian farm–forestry systems. Results are interpreted from the standpoint of both investors and landholders. The role of baselines and transaction costs are discussed.Resource /Energy Economics and Policy,

Automated Feedback Generation for a Chemistry Database and Abstracting Exercise

Timely feedback is an important part of teaching and learning. Here we describe how a readily available neural network transformer (machine-learning) model (BERT) can be used to give feedback on the structure of the response to an abstracting exercise where students are asked to summarise the contents of a published article after finding it from a publication database. The dataset contained 207 submissions from two consecutive years of the course, summarising a total of 21 different papers from the primary literature. The model was pre-trained using an available dataset (approx. 15,000 samples) and then fine-tuned on 80% of the submitted dataset. This fine tuning was seen to be important. The sentences in the student submissions are characterised into three classes - background, technique and observation - which allows a comparison of how each submission is structured. Comparing the structure of the students' abstract a large collection of those from the PubMed database shows that students in this exercise concentrate more on the background to the paper and less on the techniques and results than the abstracts to papers themselves. The results allowed feedback for each submitted assignment to be automatically generated.Comment: 9 pages, 1 figure, 3 table

Practical use of correlation coefficients in the Social Sciences

The Pearson correlation coefficient (r) is usually the first measure of association taught at elementary statistics courses. The usual presentation includes scatterplots, computation and interpretation of r, properties, examples, and warnings about inferring causality from high association between two variables. On this last aspect, few introductory textbooks go deeper into the criteria for establishing causation, and there is a lack of convincing examples in the area of the Social Sciences. Although some textbooks give adequate explanations, most of their examples belong to the field of Biostatistics. There is a need to incorporate convincing cases of the practical use of correlation as supporting evidence of causal relationships in the Social Sciences. We contribute with two examples that could be useful for teaching purposes.FC

Music in the Sea Shell : S. S. A.

https://digitalcommons.library.umaine.edu/mmb-me/1661/thumbnail.jp

Music Composition in the 17th and 18th centuries: A Historical Analysis of how Georg Frideric Handel Participated in “Borrowing”

The primary focus in this research paper is borrowing; this means borrowing from other composers, and self-borrowing from a previous composition. It is widely accepted in scholarship that Georg Frideric Handel participated in the action of borrowing. However, there is significantly more contention among scholars surrounding both the extent of Handel’s borrowing, as well as what the appropriate modern perspective is for these actions. In this research paper our primary focus will be on Handel’s borrowings, the benefits he received from these actions, and the historical lens of borrowing in the seventeenth and eighteenth centuries

Learning Adaptive Neighborhoods for Graph Neural Networks

Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavailable, one approach is to construct or learn a latent graph structure. These methods typically fix the choice of node degree for the entire graph, which is suboptimal. Instead, we propose a novel end-to-end differentiable graph generator which builds graph topologies where each node selects both its neighborhood and its size. Our module can be readily integrated into existing pipelines involving graph convolution operations, replacing the predetermined or existing adjacency matrix with one that is learned, and optimized, as part of the general objective. As such it is applicable to any GCN. We integrate our module into trajectory prediction, point cloud classification and node classification pipelines resulting in improved accuracy over other structure-learning methods across a wide range of datasets and GCN backbones.Comment: ICCV 202