肉鸡氨基酸需要动态预测模型的评价

选用初出壳爱拔益加健康雏鸡480只,分别按氨基酸模型预测值的90%、100%、110%以及中国饲养标准配制饲粮,进行饲养和屠宰试验,以评价肉鸡氨基酸需要动态模型预测值在商业饲养条件下的应用效果.结果表明,模型用于预测肉鸡的氨基酸需要量有很好的精确度.将模型预测值降低10%配制饲粮,虽然没有影响到肉鸡的增质量(P>0.05),但显著提高了肉鸡采食量(P＜0.05),导致耗料增质量比和肉鸡腹脂率显著增加(P＜0.05).而将模型预测值提高10%,没有表现生长性能和胴体品质以及其他方面的持续改善.按模型100%预测值配制的饲粮饲养效果良好,与按中国饲养标准配制的饲粮相比,肉鸡生长性能无显著差异(P>0.05),但血清中IGF-Ⅰ 水平显著提高(P＜0.05),腹脂率显著降低(P＜0.05),总经济效益显著增加(P＜0.05).     

火锅蘸酱常温贮存过程中微生物的变化研究

为预测和制定火锅蘸酱在常温贮存条件下的货架期,研究常温下火锅蘸酱中微生物生长趋势的变化,利用DPS软件,建立Gompertz模型,然后用生产日期相近的2个组蘸酱的微生物变化验证所建立的生长模型的准确度。研究结果表明,试验中所建立的Gompertz模型能有效地拟合火锅蘸酱在常温下菌落总数的动态变化,准确度较高,能准确预测不同贮藏时间内火锅蘸酱的菌落总数,为快速、可靠地实时预测火锅蘸酱的货架期和微生物学品质,提供了一个方便有效的方法;利用该模型可以预测火锅蘸酱12个月的保质期内菌落总数小于105cfu/g。     

模拟技术在水库防洪预案中的应用(英文)

[目的]探讨模拟技术在水库防洪预案中的应用。[方法]在研究河流系统模拟相关理论的基础上,采用InfoWorks RS系统建立了河流系统洪水演进模拟数字化模型,并对校核标准洪水下泄进行了仿真模拟分析。[结果]利用基于InfoWorks RS软件的洪灾信息系统对校核标准下洪水下泄对下游的淹没情况进行模拟,得出了各断面最大流量时的各水力参数、各控制断面流量与流速度随时间的变化图、各断面水力要素随时间的变化图以及下游水库控制流域最大淹没面积风险分析图。[结论]为减少洪涝灾害造成的严重损失、制定防洪预案提供了参考。     

退化羊草草原改良研究进展

分析了羊草(Leymus chinensis)草原退化的原因,概述了常用的农艺、化学和生物措施,如浅耕翻、松土、火烧、施肥、灌溉,补播、施枯草或秸秆、石膏改良技术等,在此基础上,论述了不同技术的优缺点、适用范围与改良机制。通过改良,土壤的物理化学性状明显改善,羊草根茎得到充分发展、占据了较大的生态位且竞争能力进一步加强,同时地上植被的恢复速度显著加快,地上和地下产生了积极的正向耦合效应。最后提出了重度、中度和轻度退化羊草草原的3种综合治理模式。     

基于不同经济条件下的吉林省玉米机械化生产模式区域划分研究

为确定吉林省各市玉米机械化生产主导模式,提高玉米综合机械化水平,提出玉米生产能力、经济发展水平、农民购买能力、机械化水平及自然条件等5个因素是进行玉米机械化生产模式划分的主要因素,并选取玉米产量、人均GDP、农民人均纯收入、玉米耕种收综合机械化水平、丘陵山区所占比重作为衡量指标。利用Q-型系统聚类分析方法,对吉林省玉米机械化生产模式进行区域分类,将吉林省划分为3个区域。在分析各区域特征的基础上,提出各区域玉米机械化生产的主导模式。     

高寒地区不同密度下玉米通透栽培与常规栽培比较研究

通过在不同种植密度下通透栽培模式和普通栽培模式对极早熟玉米生育期、农艺性状、生育特性和产量的对比试验分析表明:密植通透栽培模式明显优于普通栽培模式,表现为保苗数增加,生育期提前,抗倒伏能力增强,出子率增加,百粒重较高,产量差异显著。密度为8.25万株/hm²时产量差异最大,相差1032.75kg/hm²。通过试验表明,在极早熟玉米栽培中,同一品种的栽培密度通透栽培应较普通栽培密度增加7500株/hm²。     

东南沿海湿地松防护林胸径树高关系研究

为了给沿海防护林的保护和建设提供理论依据,采用样地调查的方法对东南沿海湿地松防护林进行调查,应用概率密度函数中运用最广的Logistics方程、Gompertz方程、Weibull方程、S曲线、二次曲线、Richards方程6种理论生长方程,拟合湿地松胸径与树高的关系。结果表明：Logistics方程的拟合效果最好,相关系数最大（0.9618）,剩余平方和最小（96.599）,更能准确描述湿地松的胸径树高生长特性,更适合于建立湿地松的胸径树高生长模型。Welbull模型拟合效果稍逊Logistics模型。本研究是东南沿海湿地松胸径树高模型,该模型也可尝试应用到其他防护林类型森林。     

水稻本田氮磷钾优化施肥模式研究

同等施肥量下,不同的施肥模式或将导致不同的施肥效应,形成不同的产量水平。本项研究针对水稻的基肥、蘖肥、穗肥、粒肥等环节,设置了四种氮磷钾的施用方案,相对应的产量构成因素--穗数/穴、成粒/穗、千粒重＂各有千秋、此消彼长＂。试验结果显示,处理2的施肥模式为最佳,获得了最高的产量。     

Parameter estimation and validation of a tractor and single axle towed implement dynamic system model

Developing models and estimating model parameters for a tractor and implement system is important for rapid development and improvement of precision implement guidance systems. A dynamic model for a tractor with a towed implement was developed. This model contained a tire lateral force model with several parameters that are varying and difficult to measure accurately. Several field experiments were conducted to collect trajectory data for a tractor with a single axle grain cart. Data from these experiments were used to estimate the tire lateral force model parameters. Three different replicates of the experimental trajectories were collected with each of the step, random and chirp steering inputs. The data was collected at 4.5 m/s forward velocity in two different fields. A two-step optimization process was used to estimate the tire model parameters. First, the experimental data and a set of steady state model equations were used to estimate cornering stiffness parameters. Second, a prediction error minimization method and a dynamic model were used to estimate relaxation length parameters. The parameter estimation process was repeated with each replicate of the experimental data, and the individual estimates were combined using a weighted averaging method. The vehicle model responses with estimated parameters represented the system responses with reasonable accuracy. With the parameters estimated from three different trajectories, the RMSEs for trajectories of tractor and implement CGs varied from 0.05 to 0.83 m. The model-based frequency responses also closely matched with the experimental frequency responses.     

A data-driven model simulating primary infection probabilities of cucumber downy mildew for use in early warning systems in solar greenhouses

Treatment during the primary infection phase is essential for controlling cucumber downy mildew in solar greenhouses. An early warning model applicable to this phase would represent a foundation for early warning systems for managing the disease and reducing pesticide usage. Based on the input parameters that were both readily available and appropriately limited in number, EWMPICDW (early warning model for primary infection of cucumber downy mildew in solar greenhouses) was developed based on monitoring data, early warning theory and plant disease epidemiology. The elaboration of this model included clarification of the meaning of warning, monitoring the warning indicators, forecasting the warning situation, tracing the warning sources and controlling the warning situation. The definition of warning included disease occurrence (yes or no) and probability. Because the leaf wetness duration (LWD) played an important role in disease warning systems for crops in solar greenhouses and was difficult to monitor, the leaf wetness sensor and RH threshold model were investigated and combined to form a practical estimation solution for LWD. Within the warning situation forecasting model system, the infection condition and incubation early warning submodels received the most attention. The infection condition early warning submodel was developed by using a threshold method based on the combination of LWD and mean temperature in LWD. The temperature was chosen as the warning indicator for incubation, and the incubation early warning submodel was defined using nonlinear regression methods. The warning sources traceability algorithm was developed in relation to expert knowledge and in terms of a mode of disaster mitigation that involved cutting the disaster chain from the headstream. The method for controlling the warning situation was based on good agricultural practices (GAP). The early warning model was implemented as a system and was evaluated using data for 4 years at two sites in Beijing, China. The warnings can be provided more than 2 d before symptoms appear. Using EWMPICDW, a positive early warning is associated with a change in the chance of disease occurrence from 0.68 to 0.96. Accordingly, the probability of disease occurrence calculated for the early warning model was 96%. These results demonstrate that the data-driven model will support the development of early warning systems for primary infection by cucumber downy mildew in solar greenhouses.