体外产气法评价全株藜麦与全株玉米混合青贮饲料饲用价值

为提高全株藜麦的饲料化利用率，寻找其与全株玉米的最佳青贮比，试验共设全株藜麦与全株玉米比为100：0，90：10，80：20等11个组合进行青贮发酵，发酵60 d后测定各组青贮料的体外产气发酵指标，使用灰色关联度分析筛选出最佳青贮比例。结果表明，体外发酵至36 h时，各处理组GP趋于稳定，体外发酵减弱接近停止；全株藜麦与全株玉米比例为90：10时，该青贮饲料组合的粗蛋白（CP）和粗脂肪（EE）含量显著高于其他各组（P<0.05），乙酸（AA）、总挥发性脂肪酸（TVFA）含量以及氨态氮（NH₃-N）浓度显著高于其他各处理（P<0.05）。分析表明，全株藜麦与全株玉米比例为90：10时，混合青贮饲料的营养品质和产气发酵特性综合表现最好，可作为优质混合青贮饲料在家畜养殖中推广使用。     

大豆灰斑病Fuzzy测报模型

本文运用模糊数学理论建立了大豆灰斑病预测预报模型。经对国营350农场种植的大豆中熟品种“合丰25”十年的资料拟合,符合率达90%,对中熟品种“黑农26”九年的资料拟合,符合率达89%,对早熟品种“黑河3号”九年的资料拟合,符合率达78%。     

西乡县桑元乡畜牧业发展的主要影响因素分析

应用灰色关联度分析法,对西乡县桑元乡畜牧业发展的影响因素进行分析,找出了影响该乡畜牧业发展的主要因素,并结合本地的实际情况进行分析,提出了促进该乡畜牧业发展的措施。     

应用积分GM(1,1)模型进行杉木炭疽病预测预报的研究

以积分生成代替传统的累加生成方法 ,并用双向差分建立积分GM (1,1)灰色预测模型进行杉木炭疽病预测预报的研究 ,结果表明积分GM (1,1)模型能较好地预测杉木炭疽病在三明市的发生     

拖拉机集材对迹地土壤条件及苗木生长影响的研究(Ⅱ)——拖拉机对集材道苗木生长影响的灰色动态分析

履带式拖拉机和轮式拖拉机由于其走行机构的不同,其集材道土壤压实也存在差异,特别衡量土壤压实的两个重要指标——土壤硬度和孔隙度——两种机型不一致。通过对调查测定数据进行计算处理以及用灰色系统理论进行动态分析,结果表明:与轮式拖拉机相比,履带式拖拉机对苗木生长是有利的。     

WEPP模型中的CLIGEN与BPCDG应用对比研究

通过对遂宁组紫色土产流、产沙侵蚀的观测,运用迄今为止最为复杂的土壤侵蚀预测模型WEPP模型进行单次降雨侵蚀预测,与实测值比较,并对比通过气候生成器CLIGEN和断点生成器BPCDG的产生的气候参数对预测值的影响。通过分析比较结果,认为WEPP模型对遂宁组紫色土侵蚀过程预测合理,而且利用断点生成器BPCDG的预测结果要优于气候生成器CLIGEN的预测结果。     

河北省棉花产量灰色理论分析与预测

为了解河北省棉花产量变化的规律,明确今后育种工作的目标和前景,利用灰色系统理论的GM(1,1)模型,依据1980～2003年河北省审定的棉花品种的区域试验产量,建立预测模型为:^X(1)(k 1)=38965.718385e0.024221-37990.718385。并在生产因素保持相对稳定的条件下,据此模型对河北省棉花生产进行规划性预测,为生产的规划与决策提供科学信息。明确棉花育种的总体目标是优化品种和品质结构,保持适纺中支纱品种选育的优势,重视发展目前市场短缺的长绒和中短绒陆地棉新品种选育。     

应用灰色关联度分析法综合评价高羊茅品种初探

应用灰色关联度分析法,对2000～2002年本院引进的13个高羊茅品种的适应性鉴定结果进行评估,其评估结果与品种的实际表现基本一致,说明应用关联度分析法综合评价草坪品种是可行的。分析结果:皇后、贝克、上南都的关联系数与理想品种最接近,是种植高羊茅草坪的首选品种。     

Landscape patterns as habitat predictors: building and testing models for cavity-nesting birds in the Uinta Mountains of Utah,USA

The ability to predict species occurrences quickly is often crucial for managers and conservation biologists with limited time and funds. We used measured associations with landscape patterns to build accurate predictive habitat models that were quickly and easily applied (i.e., required no additional data collection in the field to make predictions). We used classification trees (a nonparametric alternative to discriminant function analysis, logistic regression, and other generalized linear models) to model nesting habitat of red-naped sapsuckers (Sphyrapicus nuchalis), northern flickers (Colaptes auratus),tree swallows (Tachycineta bicolor), and mountain chickadees (Parus gambeli) in the Uinta Mountains of northeastern Utah, USA. We then tested the predictive capability of the models with independent data collected in the field the following year. The models built for the northern flicker, red-naped sapsucker, and tree swallow were relatively accurate (84%, 80%, and 75% nests correctly classified,respectively)compared to the models for the mountain chickadee (50% nests correctly classified). All four models were more selective than a null model that predicted habitat based solely on a gross association with aspen forests. We conclude that associations with landscape patterns can be used to build relatively accurate, easy to use, predictive models for some species. Our results stress, however, that both selecting the proper scale at which to assess landscape associations and empirically testing the models derived from those associations are crucial for building useful predictive models. This revised version was published online in July 2006 with corrections to the Cover Date.