Insight棉花产量监测系统及应用研究

棉花产量空间分布图能集中反映农田时空变量对棉花的影响,为科学调节投入和制定管理决策措施提供重要依据。研究了美国AgLeader公司Insight棉花产量监测系统的组成及工作原理,并利用该系统对试验地块棉花产量进行了测算;详细描述了Insight测产系统的设置与标定过程,并应用AFS农田空间管理系统生成了试验地块的产量空间分布图。结果表明,该图给出的棉田产量分布差异与实地调查分析结果基本吻合。     

基于模型和3S技术的稻麦产量监测预报系统

从稻麦生产管理的信息化角度出发,运用定量遥感技术、作物模拟技术和GIS技术,依据遥感信息一作物模型一长势和产量一气候环境的系统动态关系,以定量遥感反演及其与作物模型的耦合为主线,分析遥感光谱特征、农学参数与生态环境因素间的动态关系,明确影响稻麦光谱特征的主要气候环境因子,建立基于主要气候因子的农学参数遥感反演模型;     

基于卫星遥感与气象站数据的水稻高温热害监测和评估模型研究——以江苏、安徽为例

随着全球气候变暖,水稻高温热害的发生愈加频繁。为此,本文在前人研究成果的基础上,开展了水稻高温热害监测和评估模型研究。首先利用卫星遥感数据反演逐日最高气温和平均气温,云覆盖区域则以相应站点气温数据插值后补充,生成"卫星—插值"气温时间序列数据。同时提取水稻种植区域并判别其是否在高温热害的关键期——抽穗开花期。然后基于以上数据,依据水稻高温热害指标展开水稻高温热害监测和评估,对热害进行等级划分与统计。模型可实现任意时间点之前水稻高温热害的快速监测与评估,也可以给出全研究区域水稻全生育期总体的高温热害监测与评估结果。以江苏、安徽两省为例进行2013年夏季水稻高温热害监测和评估模型的应用,该模型达到了较好的使用效果,将有很好的应用前景。     

花生联合收获机实时测产控制系统设计

为了解决现有的谷物测产方法无法直接应用于花生收获过程中的实时测产,以及国外的测产装置无法直接应用于国内的花生收获机械的问题,研发了一种安装于我国自主研制的4HBL-2型系列花生联合收获机的花生收获实时测产控制系统,提出了一种新的测产机械结构,并进行了测产系统的软、硬件开发。该系统能够实现花生产量的自动测量、测产信息的存储、GPS定位和远程信息管理等功能。所研发的测产系统具有体积小、安装方便,抗干扰性强的特点。可直接安装于我国自主研制的4HBL-2型系列花生联合收获机上进行花生的实时测产,也可经改造后安装用于其它的花生联合收获机上。     

河南省粮食单产影响因素分析及变化趋势预测

利用灰色系统理论对1999—2008年的河南省粮食单产影响因素进行灰色关联分析,运用GM(1,1)模型对其发展进行关联分析,动态研究了河南省粮食生产发展趋势,为河南省粮食稳产增产提供理论依据.     

Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield

The aim of the research was to create a prediction model for winter rapeseed yield.The constructed model enabled to perform simulation on 30 June,in the current year,immediately before harvesting.An artificial neural network with multilayer perceptron(MLP) topology was used to build the predictive model.The model was created on the basis of meteorological data(air temperature and atmospheric precipitation) and mineral fertilization data.The data were collected in the period 2008–2017 from 291 productive fields located in Poland,in the southern part of the Opole region.The assessment of the forecast quality created on the basis of the neural model has been verified by defining forecast errors using relative approximation error(RAE),root mean square error(RMS),mean absolute error(MAE),and mean absolute percentage error(MAPE) metrics.An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data.The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure,which was 9.43%.The performed sensitivity analysis of the network examined the factors that have the greatest impact on the yield of winter rape.The highest rank 1 was obtained by an independent variable with the average air temperature from 1 January to 15 April of 2017(designation by the T1-4_CY model).     

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.     

基于GWR模型的渭北黄土旱塬粮食单产空间分异 及其影响因子分析——以陕西彬县为例

【目的】 通过探究渭北黄土旱塬区粮食单产在县域尺度上的空间分异特征及其影响因子,为小尺度粮食单产及其影响因子的空间分异研究、区域粮食单产提高提供科学依据。【方法】 应用空间自相关、最小二乘法和地理加权回归模型（GWR）,研究渭北黄土旱塬区典型粮食主产县陕西彬县粮食单产的空间分布特征及其影响因子的空间分异。【结果】 彬县粮食单产的Moran’s I指数为0.328,显著性检验的Z值为5.51,呈北高南低的局部空间集聚特征。坡度、耕层厚度、土壤有机质、道路密度和施肥成本对彬县粮食单产具有正向影响,土壤类型、侵蚀程度和地下水埋深对彬县粮食单产具有负向影响,各解释变量回归系数的相对极差范围为0.55—14.11。空间上,耕层厚度、土壤类型、侵蚀程度、土壤有机质和道路密度对彬县南部、东南部梁峁丘陵沟壑区粮食单产的影响强于北部黄土旱塬区,而坡度、地下水埋深和施肥成本则表现出相反的空间非平稳性特征。OLS模型回归系数的显著性与GWR模型回归系数的相对极差呈负相关关系。GWR模型的R ²比OLS模型提高了0.04,AIC值减少了11.04。 【结论】 彬县粮食单产之间存在显著的空间正相关关系;土壤有机质、施肥成本和地下水埋深是渭北黄土旱塬区县域粮食单产的最主要影响因子;同一影响因子在县域内的不同空间位置对粮食单产的影响程度存在较大差异,且各影响因子对粮食单产影响程度的空间非平稳性是导致OLS模型回归系数显著性水平较低的主要原因。GWR模型在空间非平稳性数据建模方面的解释能力与估计精度都优于OLS模型,且能够实现模型估计参数的空间可视化。     

市场化、财政支农与粮食产出——基于门槛模型的实证分析

为深入探索财政支农支出对粮食产出的影响规律,利用我国2000—2013年31个省份的面板数据样本,基于门槛模型对市场化背景下财政支农支出对我国粮食产出的影响及其区域差异进行了实证分析。结果表明:1)财政支农支出对粮食产出的影响受市场化水平的严重制约,表现出显著的非线性,存在双门槛效应,2个市场化门槛值分别为4.465及8.540。2)到2013年为止,我国已经有10个省份进入高市场化行列,19个省份为中等市场化省份,但是仍然有西藏、青海2个省份没有迈过低市场化门槛。3)财政支农支出的粮食产出效应存在显著的区域差异。在高市场化省份,财政支农支出对粮食产出的影响系数为0.393,具有显著的促进作用;在中等市场化省份,财政支农支出能够促进粮食产出,其影响系数为0.031,但是促进作用不显著;而在低市场化省份,财政支农支出对粮食产出的影响系数是-0.087,其效应显著为负。认为应该在市场化水平比较低的省份加快其市场化进程,进行制度创新,以更有效地实现财政支农促进粮食安全的政策目标。