Evaluation of agricultural ecosystem services in fallowing land based on farmers' participation and model simulation

Fallowing with green fertilizer can benefit agricultural ecosystem services (AES). Farmers in Taiwan do not implement fallow practices and plant green fertilizer because the current subsidy level (46,000 NT$per ha) is too low to manage fallowing. This paper defines the objective of government agriculture policy or the farmer’s objective as maximization of farm productivity, approximated to the value of social welfare and AES. Farms, which do not follow proper fallowing practices, often have poorly maintained fallow land or left farmland abandoned. This results in negative environmental consequences such as cutworm infestations in abandoned land, which in turn can affect crops in adjacent farmlands. The objectives of this study are twofold. First, it determines the proper fallowing subsidy based on the concept of payment for ecosystem services to entice more farmers to participate in fallowing. Second, it simulates the benefit of planting green manure in fallow land to the supply of AES based on the rate of farmers who are willing to participate in fallow land practices and essential parameters that can affect soil fertility change. The approach involves a series of interviews and a developed empirical model. The value of AES when the rate of farmer participation is 100$perha)istoolowtomanagefallowingThispaperdefinestheobjectiveofgovernmentagriculturepolicyorthefarmer’sobjectiveasmaximizationoffarmproductivityapproximatedtothevalueofsocialwelfareandAESFarmswhichdonotfollowproperfallowingpracticesoftenhavepoorlymaintainedfallowlandorleftfarmlandabandonedThisresultsinnegativeenvironmentalconsequencessuchascutworminfestationsinabandonedlandwhichinturncanaffectcropsinadjacentfarmlandsTheobjectivesofthisstudyaretwofoldFirstitdeterminestheproperfallowingsubsidybasedontheconceptofpaymentforecosystemservicestoenticemorefarmerstoparticipateinfallowingSeconditsimulatesthebenefitofplantinggreenmanureinfallowlandtothesupplyofAESbasedontherateoffarmerswhoarewillingtoparticipateinfallowlandpracticesandessentialparametersthatcanaffectsoilfertilitychangeTheapproachinvolvesaseriesofinterviewsandadevelopedempiricalmodelThevalueofAESwhentherateoffarmerparticipationis100 ) over the value at the current participation rate of 14%. This study further concludes that the appropriate fallowing subsidy has a large positive impact on AES and social welfare (e.g., benefit from food and biofuel supplies) and is seen as a basis of ecological governance for sustainable agro-ecosystems

In situ structures of the segmented genome and RNA polymerase complex inside a dsRNA virus.

Viruses in the Reoviridae, like the triple-shelled human rotavirus and the single-shelled insect cytoplasmic polyhedrosis virus (CPV), all package a genome of segmented double-stranded RNAs (dsRNAs) inside the viral capsid and carry out endogenous messenger RNA synthesis through a transcriptional enzyme complex (TEC). By direct electron-counting cryoelectron microscopy and asymmetric reconstruction, we have determined the organization of the dsRNA genome inside quiescent CPV (q-CPV) and the in situ atomic structures of TEC within CPV in both quiescent and transcribing (t-CPV) states. We show that the ten segmented dsRNAs in CPV are organized with ten TECs in a specific, non-symmetric manner, with each dsRNA segment attached directly to a TEC. The TEC consists of two extensively interacting subunits: an RNA-dependent RNA polymerase (RdRP) and an NTPase VP4. We find that the bracelet domain of RdRP undergoes marked conformational change when q-CPV is converted to t-CPV, leading to formation of the RNA template entry channel and access to the polymerase active site. An amino-terminal helix from each of two subunits of the capsid shell protein (CSP) interacts with VP4 and RdRP. These findings establish the link between sensing of environmental cues by the external proteins and activation of endogenous RNA transcription by the TEC inside the virus

Comparison of Genetic Algorithm Based Support Vector Machine and Genetic Algorithm Based RBF Neural Network in Quantitative Structure-Property Relationship Models on Aqueous Solubility of Polycyclic Aromatic Hydrocarbons

AbstractA modified method to develop quantitative structure-property relationship (QSPR) models of organic contaminants was proposed based on genetic algorithm (GA) and support vector machine (SVM). GA was used to perform the variable selection and SVM was used to construct QSPR model. In this study, GA-SVM was applied to develop the QSPR model for aqueous solubility (Sw, mg•l-1) of polycyclic aromatic hydrocarbons (PAHs). The R2 (0.980), SSE (2.84), and RMSE (0.25) values of the model developed by GA-SVM indicated a good predictive capability for logSw values of PAHs. Based on leave-one-out cross validation, the results of GA-SVM were compared with those of genetic algorithm-radial based function neural network (GA-RBFNN). The comparison showed that the R2 (0.923) and RMSE (0.485) values of GA-SVM were higher and lower, respectively, which illustrated GA-SVM was more suitable to develop QSPR model for the logSw values of PAHs than GA-RBFNN

Innovation strategy of science and technology in Korea

노트 : 16th International Conference on Composite Material

Thermal management of the through silicon vias in 3-D integrated circuits

The through silicon via technology is a promising and preferred way to realize the reliable interconnection for 3-D integrated circuit integration. However, its size and the property of the filled-materials are two factors affecting the thermal behavior of the integrated circuits. In this paper, we design 3-D integrated circuits with different through silicon via models and analyze the effect of different material-filled through silicon vias, aspect ratio and thermal conductivity of the dielectric on the steady-state temperature profiles. The results presented in this paper are expected to aid in the development of thermal design guidelines for through silicon vias in 3-D integrated circuits

New Method for Numerically Solving the Chemical Potential Dependence of the Dressed Quark Propagator

Based on the rainbow approximation of Dyson-Schwinger equation and the assumption that the inverse dressed quark propagator at finite chemical potential is analytic in the neighborhood of $\mu=0$, a new method for obtaining the dressed quark propagator at finite chemical potential $\mu$ from the one at zero chemical potential is developed. Using this method the dressed quark propagator at finite chemical potential can be obtained directly from the one at zero chemical potential without the necessity of numerically solving the corresponding coupled integral equations by iteration methods. A comparison with previous results is given.Comment: Revtex, 14 pages, 5 figure