Study of Ontology-Based Swine Diagnosis Technology

The computer swine disease diagnosis is an important tool for swine farming industry, but the traditional expert system cannot meet the requirement of practical application. To improve the situation, a swine disease ontology is constructed, which can model the knowledge of swine disease diagnosis into a concept system, and a mechanism that can save the ontology into relational database is established, further more a computer system is developed to implement ontology- based swine disease diagnosis, so make the diagnosis results extended and more precise.

Extreme meteorological disaster effects on grain production in Jilin Province, China

Extreme meteorological disaster effects on grain production is mainly determined by the interaction between danger degree of hazard-induced factors and vulnerability degree of hazard-affected bodies. This paper treats physical exposure, sensitivity of the response to the impact, and capabilities of disaster prevention and mitigation as a complex system for vulnerability degree of hazard-affected bodies, which included the external shocks and internal stability mechanism. Hazard-induced factors generate external shocks on grain production systems though exposure and sensitivity of hazard-affected body, and the result can be represented as affected area of grain. By quantile regression model, this paper depicts the quantitative relationship between hazard-induced factors of extreme meteorological disaster and the affected area in the tail of the distri- bution. Moreover, the model of production function have also been utilized to expound and prove the quantitative relationship between the affected area and final grain output under the internal stability mechanism of the agricultural natural resources endowment, the input factors of agricultural production, and the capacity of defending disaster. The empirical study of this paper finds that impact effects of drought disaster to grain production system presents the basic law of ＂diminishing marginal loss＂, namely, with the constant improvement of the grade of drought, marginal affected area produced by hazard-induced factors will be diminishing. Scenario simulation of extreme drought impact shows that by every 1% reduction in summer average rainfall, grain production of Jilin Province will fell 0.2549% and cut production of grain 14.69% eventually. In re- sponse to ensure China＇s grain security, the construction of the long-term mechanism of agricultural disaster prevention and mitigation, and the innovation of agricultural risk management tools should be also included in the agricultural policy agenda.

Global sensitivity analysis of wheat grain yield and quality and the related process variables from the DSSAT-CERES model based on the extended Fourier Amplitude Sensitivity Test method

A crop growth model,integrating genotype,environment,and management factor,was developed to serve as an analytical tool to study the influence of these factors on crop growth,production,and agricultural planning.A major challenge of model application is the optimization and calibration of a considerable number of parameters.Sensitivity analysis(SA) has become an effective method to identify the importance of various parameters.In this study,the extended Fourier Amplitude Sensitivity Test(EFAST) approach was used to evaluate the sensitivity of the DSSAT-CERES model output responses of interest to 39 crop genotype parameters and six soil parameters.The outputs for the SA included grain yield and quality(take grain protein content(GPC) as an indicator) at maturity stage,as well as leaf area index,aboveground biomass,and aboveground nitrogen accumulation at the critical process variables.The key results showed that:(1) the influence of parameter bounds on the sensitivity results was slight and less than the impacts from the significance of the parameters themselves;(2) the sensitivity parameters of grain yield and GPC were different,and the sensitivity of the interactions between parameters to GPC was greater than those between the parameters to grain yield;and(3) the sensitivity analyses of some process variables,including leaf area index,aboveground biomass,and aboveground nitrogen accumulation,should be performed differently.Finally,some parameters,which improve the model’s structure and the accuracy of the process simulation,should not be ignored when maturity output as an objective variable is studied.