Pre-Harvest Price Expectations for Corn: The Information Content of USDA Reports and New Crop Futures

This article examines the puzzle of why futures prices continue to react to USDA crop reports despite the fact that reports appear to be no longer "newsworthy," that is, provide no better production estimates than private forecasts. The information value of reports is measured in terms of their influence on rational agents' harvest-time corn price expectations, which are uncovered using a Hamilton-type modeling approach. Results show that reports are still "newsworthy," as they would contribute to agents' price expectations if released a day early. Thus futures price reactions, which closely reflect price expectations, are rational and consistent with efficient markets hypothesis.     

Modeling Conditional Yield Densities

Given the increasing interest in agricultural risk, many have sought improved methods to characterize conditional crop-yield densities. While most have postulated the Beta as a flexible alternative to the Normal, others have chosen nonparametric methods. Unfortunately, yield data tends not to be sufficiently abundant to invalidate many reasonable parametric models. This is problematic because conclusions from economic analyses, which require estimated conditional yield densities, tend not to be invariant to the modeling assumption. We propose a semiparametric estimator that, because of its theoretical properties and our simulation results, enables one to empirically proceed with a higher degree of confidence.     

Estimating the Impact of Transgenic Bt Cotton on West and Central Africa: A General Equilibrium Approach

West and Central Africa (WCA)’s cotton sector is experiencing rising production costs and lower yields, reversing decades of growth. Declining input use, soil fertility and inefficient chemical pest controls are contributing factors. We evaluate the potential impact of Bt cotton on WCA using a multiregion general equilibrium model and multicountry estimates of Bt-induced productivity. We find that Bt cotton raises growers’ returns, land value and welfare. Released labor from cotton is shifted to food crops hence reducing labor shortage constraints. Overall, results indicate that potential gross benefits from Bt cotton are substantial for WCA cotton sector, and that the economic costs of nonadoption are equally significant.     

渐开线圆柱齿轮的形位误差探讨

从误差角度分析，综述ＧＢ１００９５－８８《渐开线圆柱齿轮精度》误差的表达方法。     

Input use under cost-of-production crop insurance: Theory and evidence

There have been a number of previous studies that examined the effects of yield- or revenue-based crop insurance products on input use of farmers. However, no study has specifically investigated the input use impacts of a cost-of-production (COP) crop insurance policy, even though this type of crop insurance is the predominant one used in several other countries outside of the United States (such as the Philippines and China). This article aims to theoretically and empirically examine the effect of a COP crop insurance product on farmers’ chemical input use. Our theoretical model suggests that the effect of COP insurance on input use can either be positive or negative, with the resulting impact depending on the strengths of (a) the traditional moral hazard effect of insurance (i.e., an input use decreasing effect); versus (b) the marginal incentives to apply more inputs due to input levels being the main determinant for expected indemnity amounts in this type of insurance (i.e., an input use increasing effect). A survey data set from corn farmers in the Philippines is then used to empirically illustrate how a particular COP insurance product influences input use in a real-life context. In this case, we find that COP insurance increases the use of chemical inputs (e.g., fertilizers and total chemical expenditure), implying that the positive marginal incentive to apply more inputs dominates the negative moral hazard effect.     

我国政策性农业保险实施效果评价

根据在政策性农业保险试点地区进行实地调研所得的数据,对农户对我国政策性农业保险实施效果的评价结果进行了描述性统计分析。调研结果表明:不同地区的农户对农业保险的需求程度不同,因此其对农业保险实施效果的评价结果也不尽相同;总体来讲,农业保险在稳定农作物产量、稳定农户家庭收入、增强农业抵御风险能力等方面确实发挥了一定的作用,但还未具有明显的长期效果,并未得到广泛认可。     

深度学习方法在作物遥感分类中的应用和挑战

[目的]准确估算作物的面积和分布对粮食安全至关重要。与传统的机器学习方法相比,深度学习具有多种优势,如端到端训练、可迁移性。为有效利用高时空数据进行作物识别提供了新的机遇。已有多种模型被应用于作物分类任务中,针对不同的分类任务,如何有效地选择模型,并对其进行训练和使用已成为关键问题。[方法]文章回顾了利用深度学习模型对作物分类的主要研究。N维卷积神经网络(N-D CNN)(N=1、2、3)和递归神经网络(RNN)已被有效用于作物分类任务。长短期记忆RNN(LSTM RNN)和门控循环单元RNN(GRU RNN)是RNN的变体,解决了随着时间序列增加RNN出现的梯度消失或爆炸问题。此外,还有研究使用CNN和RNN(我们称为RCNN)的混合模型对作物进行分类。该文首先阐述了使用深度学习方法进行作物制图的背景和意义,并介绍了CNN和RNN模型结构。然后回顾了一些典型的研究,包括模型的结构、遥感数据源、数据处理方法和分类精度。最后,总结了使用深度学习方法进行作物分类的挑战以及现有解决方案的局限性。[结果](1)1-D CNN可用于提取时间特征,或时间+光谱特征,分类效果良好;2-D CNN已被广泛应用于单时相数据的空间特征提取,分类精度依赖于数据源;3-D CNN应用较少,但具有很大的潜力,尤其是时间+空间维度的特征提取;(2)相同条件下(架构、数据源、研究区域、类别),LSTM RNN和GRU RNN分类效果通常高于普通RNN,而前两者的效果差距不大,但GRU RNN训练时间较短;(3)CNN+RNN混合模型(RCNN)用RNN比3-D CNN更适合提取时间特征。这主要是由于RNN建立了对序列数据的长期依赖,而3-D CNN卷积核是局部计算的。[结论]通过分析,认为深度学习技术是作物遥感分类的有效工具。此外,与其他模型相比,RCNN,3-D CNN和GUR RNN具有更大的潜力。     

我国农作物秸秆综合利用产业促进政策研究

［目的］深入地分析了我国秸秆综合利用产业现行促进政策,阐述了存在的主要问题,并提出针对性解决方法,以期为促进秸秆综合利用政策体系建设提供政策建议。［方法］按照法律法规、目标引导和考核、财税补贴、技术研发和推广、信息统计等进行分类,综合评述各类现行政策的特点、使用范围和有效性,并采用相关者利益分析等方法分析存在问题,提出政策建议。［结果］随着我国秸秆露天焚烧问题日益突出,国家加大了对秸秆综合利用重视的程度,出台一系列关于秸秆综合利用等方面的法律、法规和激励政策等,初步构建了秸秆综合利用政策体系,呈现出聚焦关键环节、关键技术和重点地区的趋势。但是,还存在着政策的有效性有限,未形成合力; 农民参与度低; 使用环节缺乏激励政策等问题。［结论］建议出台区域生态补偿制度,落实耕地地力保护补贴政策,鼓励终端产品使用和消费等政策。     

我国农作物秸秆综合利用现状及焚烧碳排放估算

[目的]为加快推进秸秆资源化利用工作,有效缓解因秸秆焚烧带来的环境问题。[方法]将全国划分为黄淮海区、西北区、东北区、东南区、西南区五大区域,对各类农作物秸秆进行了统计分析,研究了秸秆产生量、秸秆资源密度、秸秆利用结构、秸秆焚烧碳排放量。[结果]全国秸秆理论资源量达到10.4亿t,可收集量达到9.0亿t,玉米、水稻和小麦3类农作物秸秆占总量的79.19%,黄淮海区秸秆产量最高,占总量的33.41%。秸秆已利用量为7.21亿t,综合利用率达到80.11%,其中肥料化、饲料化、基料化、燃料化、原料化利用量占已利用量的比例分别为53.93%、23.42%、4.98%、14.27%、3.40%,形成了农用为主的利用格局,但不同秸秆种类综合利用率和利用结构存在显著差异。全国秸秆资源密度和人均占有量分别为523.57kg/667m~2和1.75 t,东北区显著高于全国平均水平,西南区和东南区则显著低于全国平均水平。根据环保部卫星遥感监测的露天焚烧火点数比例估算,2015年我国秸秆露天焚烧量约为8 110万t,总碳排放量约为3 450万t,其中东北地区约占74.1%。[结论]我国秸秆综合利用的重点和难点在东北地区,进一步推动区域秸秆实现全量利用,对于促进低碳农业发展,应对气候变化有着十分重要的意义。