我国食品价格对非食品价格的传导效应——基于VAR模型的实证分析

近年来,我国食品通胀率持续上涨,而非食品通胀率也呈现出不断增长的趋势,由此引发了社会的广泛关注,本文实证研究了食品通胀对非食品通胀的传导效应.结果表明：1）非食品价格和食品价格之间存在相互影响的双向因果关系;2）食品价格的上涨会对非食品价格产生正向冲击,且这种冲击有一定的时滞性,非食品价格受到食品价格冲击的影响比较明显;3）平均来看,2007年1月-2012年12月,食品价格冲击对非食品价格的变动有明显解释力,但非食品价格的变动主要由其自身冲击造成.     

PhenologyMMS: A program to simulate crop phenological responses to water stress

Crop phenology is fundamental for understanding crop growth and development, and increasingly influences many agricultural management practices. Water deficits are one environmental factor that can influence crop phenology through shortening or lengthening the developmental phase, yet the phenological responses to water deficits have rarely been quantified. The objective of this paper is to provide an overview of a decision support technology software tool, PhenologyMMS V1.2, developed to simulate the phenology of various crops for varying levels of soil water. The program is intended to be simple to use, requires minimal information for calibration, and can be incorporated into other crop simulation models. It consists of a Java interface connected to FORTRAN science modules to simulate phenological responses. The complete developmental sequence of the shoot apex correlated with phenological events, and the response to soil water availability for winter and spring wheat (Triticum aestivum L.), winter and spring barley (Hordeum vulgare L.), corn (Zea mays L.), sorghum (Sorghum bicolor L.), proso millet (Panicum milaceum L.), hay/foxtail millet [Setaria italica (L.) P. Beauv.], and sunflower (Helianthus annus L.) were created based on experimental data and the literature. Model evaluation consisted of testing algorithms using “generic” default phenology parameters for wheat (i.e., no calibration for specific cultivars was used) for a variety of field experiments to predict developmental events. Results demonstrated that the program has general applicability for predicting crop phenology and can aid in crop management.     

Improving cereal yield forecasts in Europe – The impact of weather extremes

The impact of extreme events (such as prolonged droughts, heat waves, cold shocks and frost) is poorly represented by most of the existing yield forecasting systems. Two new model-based approaches that account for the impact of extreme weather events on crop production are presented as a way to improve yield forecasts, both based on the Crop Growth Monitoring System (CGMS) of the European Commission. A first approach includes simple relations – consistent with the degree of complexity of the most generic crop simulators – to explicitly model the impact of these events on leaf development and yield formation. A second approach is a hybrid system which adds selected agro-climatic indicators (accounting for drought and cold/heat stress) to the previous one. The new proposed methods, together with the CGMS-standard approach and a system exclusively based on selected agro-climatic indicators, were evaluated in a comparative fashion for their forecasting reliability. The four systems were assessed for the main micro- and macro-thermal cereal crops grown in highly productive European countries. The workflow included the statistical post-processing of model outputs aggregated at national level with historical series (1995–2013) of official yields, followed by a cross-validation for forecasting events triggered at flowering, maturity and at an intermediate stage. With the system based on agro-climatic indicators, satisfactory performances were limited to microthermal crops grown in Mediterranean environments (i.e. crop production systems mainly driven by rainfall distribution). Compared to CGMS-standard system, the newly proposed approaches increased the forecasting reliability in 94% of the combinations crop × country × forecasting moment. In particular, the explicit simulation of the impact of extreme events explained a large part of the inter-annual variability (up to +44% for spring barley in Poland), while the addition of agro-climatic indicators to the workflow mostly added accuracy to an already satisfactory forecasting system.     

Importance of habitat type classifications for predicting ruffed grouse use of areas for drumming

To incorporate ruffed grouse (Bonasa umbellus) habitat planning in forest management, it is necessary for managers to understand factors contributing to grouse habitat use. Previous studies examining ruffed grouse drumming habitat documented relationships between drumming grouse and broad vegetation categories (e.g., northern hardwoods, young aspen [Populus spp.], oak [Quercus spp.]), but few studies have documented how drumming grouse respond to ecological variations in site conditions of aspen or other vegetation types that might be used. Our objectives were to determine the utility of habitat type classifications in predicting the occurrence of ruffed grouse drumming habitat in the western Upper Peninsula of Michigan, and demonstrate how classifications may be used to understand how forest management may affect ruffed grouse habitat. We used survey routes on state land and conducted drumming surveys during mid-April and early May at 78 points in 2005 and 2006. We recorded the number of drumming males heard at each point, the azimuth to where the grouse was heard, and a qualitative measure of distance to determine in which forest stands grouse were drumming. Using GIS, we determined the specific vegetation type, age class, and habitat type, evaluated habitat suitability, and determined a suitability score for areas in which grouse were drumming. We constructed a logistic regression model that calculated the probability of grouse use of areas for drumming based on vegetation characteristics at used and random locations. Our results indicated that the probability of grouse use of an area for drumming is based on inherent site characteristics (i.e., habitat type) and habitat suitability. The model is useful for planning forest management activities and understanding how grouse may respond to spatial or temporal changes in vegetation through succession or manipulation.     

基于三端点区间数的变权模糊综合评判模型研究

为有效控制主观评价对评判结果的影响,基于三端点区间数,引入决策因子定义其期望值算子,从而提出一种三端点区问数的变权模糊综合评判模型。该模型通过综合不同偏好的决策者的评判信息给出准确、科学的评判结果。最后通过数值实例验证模型的有效性。