World Soybean Demand: An Elasticity Analysis and Long-Term Projections

Soybeans are one of the most valuable crops in the world and are characterized by their multi-purpose uses: food, feed, fuel and other industrial usages such as paint, inks, and plastics. Out of 183.9 million tons of world supply/demand of soybeans in 2001-03 year, about 10% of them were directly consumed as food (5.9%) or feed (3.8%) but 84.2% of them were crushed into soyoil and soymeal. Soyoil is mainly processed to vegetable oil for human consumption and recently used as a biodiesel feedstock. Soymeal is used not only as feed for livestock (especially for pork and poultry due to its low fiber level) and aquaculture, but also as a good source of protein for the human diet in a variety of forms in different cultures. This paper analyzes the relationship of the demand for soybeans with economy at country and international levels. We use the county level domestic demand quantities with GDP data and apply an error correction mechanism (ECM) to estimate the long-term elasticities of demand for soybeans in the market/economy. Using the estimated long-term elasticities, the demands for soybeans are projected through 2030.soybean demand, elasticity, error correction mechanism (ECM), projection, Agribusiness, Crop Production/Industries, Demand and Price Analysis, Marketing, C22, C53, Q11,

World Soybean Production: Area Harvested, Yield, and Long-Term Projections

Soybean, production, yield, land use, long-term projection, exponential smoothing with damped trend, Agribusiness, Agricultural and Food Policy, Crop Production/Industries, Land Economics/Use, Q1,

Immunization of networks with community structure

In this study, an efficient method to immunize modular networks (i.e., networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on the eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.Comment: 3 figures, 1 tabl

Voter model with non-Poissonian interevent intervals

Recent analysis of social communications among humans has revealed that the interval between interactions for a pair of individuals and for an individual often follows a long-tail distribution. We investigate the effect of such a non-Poissonian nature of human behavior on dynamics of opinion formation. We use a variant of the voter model and numerically compare the time to consensus of all the voters with different distributions of interevent intervals and different networks. Compared with the exponential distribution of interevent intervals (i.e., the standard voter model), the power-law distribution of interevent intervals slows down consensus on the ring. This is because of the memory effect; in the power-law case, the expected time until the next update event on a link is large if the link has not had an update event for a long time. On the complete graph, the consensus time in the power-law case is close to that in the exponential case. Regular graphs bridge these two results such that the slowing down of the consensus in the power-law case as compared to the exponential case is less pronounced as the degree increases.Comment: 18 pages, 8 figure

Collective fluctuations in networks of noisy components

Collective dynamics result from interactions among noisy dynamical components. Examples include heartbeats, circadian rhythms, and various pattern formations. Because of noise in each component, collective dynamics inevitably involve fluctuations, which may crucially affect functioning of the system. However, the relation between the fluctuations in isolated individual components and those in collective dynamics is unclear. Here we study a linear dynamical system of networked components subjected to independent Gaussian noise and analytically show that the connectivity of networks determines the intensity of fluctuations in the collective dynamics. Remarkably, in general directed networks including scale-free networks, the fluctuations decrease more slowly with the system size than the standard law stated by the central limit theorem. They even remain finite for a large system size when global directionality of the network exists. Moreover, such nontrivial behavior appears even in undirected networks when nonlinear dynamical systems are considered. We demonstrate it with a coupled oscillator system.Comment: 5 figure

Analysis of scale-free networks based on a threshold graph with intrinsic vertex weights

Many real networks are complex and have power-law vertex degree distribution, short diameter, and high clustering. We analyze the network model based on thresholding of the summed vertex weights, which belongs to the class of networks proposed by Caldarelli et al. (2002). Power-law degree distributions, particularly with the dynamically stable scaling exponent 2, realistic clustering, and short path lengths are produced for many types of weight distributions. Thresholding mechanisms can underlie a family of real complex networks that is characterized by cooperativeness and the baseline scaling exponent 2. It contrasts with the class of growth models with preferential attachment, which is marked by competitiveness and baseline scaling exponent 3.Comment: 5 figure