The soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is one of the greatest threats to soybean production, and both trend analysis and periodic analysis of its population dynamics are important for integrated pest management (IPM).  Based on systematically investigating soybean aphid populations in the field from 2018 to 2020, this study adopted the inverse logistic model for the first time, and combined it with the classical logistic model to describe the changes in seasonal population abundance from colonization to extinction in the field.  Then, the increasing and decreasing phases of the population fluctuation were divided by calculating the inflection points of the models, which exhibited distinct seasonal trends of the soybean aphid populations in each year.  In addition, multifactor logistic models were then established for the first time, in which the abundance of soybean aphids in the field changed with time and relevant environmental conditions.  This model enabled the prediction of instantaneous aphid abundance at a given time based on relevant meteorological data.  Taken as a whole, the successful approaches implemented in this study could be used to build a theoretical framework for practical IPM strategies for controlling soybean aphids.

The nonstructural protein 10 (nsp10) of porcine reproductive and respiratory syndrome virus (PRRSV) encodes for helicase which plays a vital role in viral replication.  In the present study, a truncated form of nsp10, termed nsp10a, was found in PRRSV-infected cells and the production of nsp10a was strain-specific.  Mass spectrometric analysis and deletion mutagenesis indicated that nsp10a may be short of about 70 amino acids in the N terminus of nsp10.  Further studies by rescuing recombinant viruses showed that the Glu-69 in nsp10 was the key amino acid for nsp10a production.  Finally, we demonstrated that nsp10a exerted little influence on the growth kinetics of PRRSV in vitro. 

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 distribution.  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 response 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.

Flood catastrophe risk assessment is imperative for the steady development of agriculture under the context of global climate change, and meanwhile, it is an urgent scientific issue need to be solved in agricultural risk assessment discipline. This paper developed the methodology of flood catastrophe risk assessment, which can be shown as the standard process of crop loss calculation, Monte Carlo simulation, the generalized extreme value distribution (GEV) fitting, and risk evaluation. Data on crop loss were collected based on hectares covered by natural disasters, hectares affected by natural disasters, and hectares destroyed by natural disasters using the standard equation. Monte Carlo simulation based on appropriate distribution was used to expand sample size to overcome the insufficiency of crop loss data. Block maxima model (BMM) approach based on the extreme value theory was for modeling the generalized extreme value distribution (GEV) of flood catastrophe loss, and then flood catastrophe risk at the provincial scale in China was calculated. The Type III Extreme distribution (Weibull) has a weighted advantage of modeling flood catastrophe risk for grain production. The impact of flood catastrophe to grain production in China was significantly serious, and high or very high risk of flood catastrophe mainly concentrates on the central and eastern regions of China. Given the scenario of suffering once-in-a-century flood disaster, for majority of the major-producing provinces, the probability of 10% reduction of grain output is more than 90%. Especially, the probabilities of more than 15% decline in grain production reach up to 99.99, 99.86, 99.69, and 91.60% respectively in Anhui, Jilin, Liaoning, and Heilongjiang. Flood catastrophe assessment can provide multifaceted information about flood catastrophe risk that can help to guide management of flood catastrophe.