农产品监测预警模型集群构建理论方法与应用

【目的】农产品供给与需求的准确分析测定,是农业监测预警能力提升的重要表现。构建产品多品种多环节模型集群理论方法,可高效解决单一环节或单一模型难以解决的分析技术难题。【方法】在农产品供需的重要要素即生产量、消费量、贸易量、价格等分析预测过程中,针对农产品品种间关联性强,自然、社会、经济诸多影响因素纠缠,模型多变量强耦合、非线性、参数时变的特点,提出多品种农产品“因素分类解耦、参数转用适配”方法,以构建多时空维度的监测预警模型集群。【结果】利用“因素分类解耦、参数转用适配”技术方法,研究构建了不同农产品的生产类、消费类、贸易类、价格类的模型集群。这些模型集群可用于对不同时空维度的水稻、玉米、小麦、肉类等主要农产品供需的长中短期的分析预测,支撑形成了农业展望中的主要农产品平衡表,其中主要农产品全国年度生产量6年平均预测精度高于97%。【结论】研究提出的农产品监测预警模型集群构建理论及其方法,有效提升了农产品多品种模型集群的求解效率和准确率,增强了农产品供需分析预测的系统性与智能性,为系统揭示农产品复杂的时空供需变化特征、促进农产品市场调控科学性和可预见性,提供了新技术方法。     

Simulating the Production of Rice Genotypes by Flood Management and End-Season Water Stress Conditions Using AquaCrop Model

ABSTRACT

The present study was conducted to assess the ability of AquaCrop model in predicting of grain and biological yield of rice genotypes in water management. A two-year field experiment was conducted at the experimental farm of the Iranian Rice Research Institute in Rasht, Iran from 2016 to 2017. The experiment was established in a split-plot design with two irrigation management (continuous submergence and end season water stress) as the main plot, fourth rice genotypes as the sub-plot and three replications. The goodness-of-fit between observed and simulated grain yield and final biomass was assessed by means of the coefficient of determination (R ²), the absolute and normalized root mean square errors (RMSE). The RMSE_n of predicting grain yield at calibration and evaluation stages was in the range of 6–12% and 6–8% for biological yield. The results indicated that AquaCrop model is suitable to predict grain yield and biological yield of rice genotypes in northern Iran. AquaCrop model can be used to determine optimization strategies to improve the water consumption of rice genotypes.     

一类具有饱和发生率的虫媒传染病模型研究

考虑虫媒传染病具有潜伏期的特征,研究了一类具有饱和发生率的时滞传染病模型的动力学行为,确定了疾病是否流行的阈值R_0.当R_01时,无病平衡点全局渐近稳定,疾病将最终灭绝;当R_01时,唯一地方病平衡点条件稳定,系统会产生Hopf分支.     

从开放科学路线图分析到开放科学道路决策方法设计

[目标/意义]一个国家是否进入开放科学、如何选择路径,亟待决策方法。[方法/过程]开放科学作为国家科学政策,可以通过路线图方式进行规划实施。分析当前6个国际开放科学路线图的发布、内容、效果评估等特征,初步提出一个国家开放科学道路的决策方法。[结果/结论]开放科学路线图的形成受国家中长期开放计划、配套基础设施等多因素影响,实施内容覆盖到国家的未来、现状和具体措施,最终将提升一个国家或地区科学研究的开放成熟度水平及其影响力。基于这些特征,初步构建国家开放科学进程决策模型,覆盖开放科学实施前、实施、实施后不同阶段的决策内容,并设计开放科学道路决策的“三步法”。本文简略介绍中国面向开放科学的工作构想,以及其中图书馆发挥的作用。     

基于启发式Johnson算法优化BP神经网络的水产养殖产量预测模型

针对水产养殖产量预测难的现状,提出一种基于启发式Johnson算法优化的反向传播神经网络(BPNN)的产量预测模型。该模型在传统BP神经网络的基础上,针对网络训练时间长、易陷入局部最优的问题,通过启发式Johnson算法降低输入神经元维度,再结合试凑法确定神经网络隐层个数,构建启发式Johnson反向传播神经网络(HJA-BPNN)学习预测模型。实验结果表明,该模型在山东省对虾海水养殖产量预测中,预测的均方根误差小于传统BP神经网络和GM(1,1),且学习效率相比传统BP神经网络有所提升。研究表明,该学习预测模型在大量历史数据的模型构造上有更大的优势,能够缩短建模时间,同时获得良好的预测效果,为水产养殖产量预测提供了一种可行的新方法。     

茶树GGPS基因家族的克隆、生物信息学和表达分析

牻牛儿基牻牛儿基焦磷酸合酶(Geranylgeranyl diphosphate synthase,GGPS)是萜类合成途径的结构酶,对植物生长发育具有重要意义。本研究通过RACE和RT-PCR方法克隆得到5条潜在的茶树GGPS序列,分别命名为CsGGPS1-4和CsGGPS9,其中CsGGPS9存在3条等位基因,分别是CsGGPS9-1、CsGGPS9-2和CsGGPS9-3,在系统进化树上与其他基因分成两支。蛋白质序列分析表明,茶树GGPS家族成员都具有polyprenyl_synt结构域,不存在信号肽序列。亚细胞定位预测结果显示,CsGGPS1、CsGGPS2和CsGGPS4定位在叶绿体上,CsGGPS3和CsGGPS9定位在线粒体上。通过Swiss Model进行三维建模,结合"three-floor"模型对茶树GGPS家族成员的功能进行预测,预测结果显示,CsGGPS1、CsGGPS2和CsGGPS4是GGPS;CsGGPS3是异源二聚体形式的牻牛儿基焦磷酸合酶的小亚基;CsGGPS9的催化主产物是碳链数大于30的异戊烯基焦磷酸。q RT-PCR分析表明,CsGGPS1整体表达丰度较低,仅在一芽二叶中表达量稍高;CsGGPS2在茶树各个组织中均有表达,在花中表达量最高,且花发育过程中表达量先上升后下降;CsGGPS3在叶和幼根中的表达量高于花,花发育过程中表达平稳;CsGGPS4在茶树各个组织中表达量数值相近,在花发育过程中表达量变化趋势与CsGGPS2相同;CsGGPS9的表达量在成熟叶中显著低于幼嫩叶片。     

A stochastic surplus production model in continuous time

Surplus production modelling has a long history as a method for managing data‐limited fish stocks. Recent advancements have cast surplus production models as state‐space models that separate random variability of stock dynamics from error in observed indices of biomass. We present a stochastic surplus production model in continuous time (SPiCT), which in addition to stock dynamics also models the dynamics of the fisheries. This enables error in the catch process to be reflected in the uncertainty of estimated model parameters and management quantities. Benefits of the continuous‐time state‐space model formulation include the ability to provide estimates of exploitable biomass and fishing mortality at any point in time from data sampled at arbitrary and possibly irregular intervals. We show in a simulation that the ability to analyse subannual data can increase the effective sample size and improve estimation of reference points relative to discrete‐time analysis of aggregated annual data. Finally, subannual data from five North Sea stocks are analysed with particular focus on using residual analysis to diagnose model insufficiencies and identify necessary model extensions such as robust estimation and incorporation of seasonality. We argue that including all known sources of uncertainty, propagation of that uncertainty to reference points and checking of model assumptions using residuals are critical prerequisites to rigorous fish stock management based on surplus production models.     

Drivers of Setaria pumila (Poir.) Roem. et Schult growth and impact on forage quality in lowland Switzerland meadows

Invasive grasses (especially Setaria pumila (Poir.) Roem. et Schult) increasingly threaten meadows and pastures as a consequence of human impact and climate change. We conducted a study in 2012 and in 2013 to better understand the growing cycle and the influence of S. pumila on forage quality of lowland meadows. We observed a rapid increase of S. pumila presence and phytomass on the southern side of the Alps across the growing season. We measured (i) above‐ground phytomass, with a modified Corral‐Fenlon method; (ii) botanical composition using the linear point quadrat method; and (iii) grassland chemical composition by NIRs (near‐infrared spectroscopy) analyses. To test the hypothesis that S. pumila summer growth is related to specific climatic conditions, meteorological data (temperature, precipitation and evapotranspiration) were collected from meteorological stations near the study sites. Total phytomass was sorted into S. pumila and other species. We used a generalized linear mixed models (GLMM) and found the abundance of S. pumila to be inversely correlated with rainfall and the presence of other species, but positively correlated with temperature increase. The increase of S. pumila above‐ground phytomass production was linked to a reduction of forage quality.     

夜间灯光数据DMSP/OLS在贫困地区亮化度模型构建中的应用

贫困县亮化度一定程度上反映着贫困县居民的生活水平发展状况,但与贫困区经济收入等相关指标的定量关系不太明确。针对该情况,提出了一种应用灯光数据的亮度统计数据,结合与贫困县经济收入等密切相关的经济规模指标、人口数量、财政收入等来定量研究贫困县亮化度模型,并选取样本贫困县对该模型进行验证。经数据验证,该模型具有较高的可信度。     

基于ARIMA模型的我国大豆单产预测分析

在我国大豆单产光合潜力和"农业生态区划"(AEZ)潜力基础上,运用ARIMA(自回归单整移动平均)模型预测了2020年前我国大豆单产。结果表明:我国大豆单产最大潜力为3 400 kg·hm~(-2),而2017、2018、2019和2020年单产将分别为1 899,1 926,1 954和1 982 kg·hm~(-2),分别是最大潜力的55.85%、56.65%、57.47%和58.29%。这意味着:未来提高我国大豆单产尚有较大空间,应保持高产耕地生产力与改良中低产田土并重。研究结果旨在为我国大豆生产提供决策参考信息。