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%。     

Validation of the accuracy of household reporting of subsistence fishing catch and effort: a Fijian case study

Abstract  The accuracy of household reporting of subsistence fishing catch and effort and seafood consumption on the Fijian island of Ono-i-Lau was studied. A creel survey was carried out concurrently to validate the household survey data. Reported estimates of fishing participation, effort, and fish consumption were not significantly different to the creel survey estimates. Householder's appeared to overestimate the number of fish from the most abundant family Lethrinidae, and underestimate rarer fish. However, there was no significant difference between the reported and observed contribution of the majority of finfish families and invertebrate taxa. The reported and observed catch rate estimates were not significantly different, indicating that Ono-i-Lau villagers are relatively accurate in their estimation of the number and size of fish from recalled catches.     

应用热脉冲技术对小美旱杨耗水量的研究

应用热脉冲对河套灌区农田防护小美旱杨（Populus popular’s）日蒸腾耗水量进行了研究。结果表明：在正常生长状态下，小美旱杨的日蒸腾耗水量变化曲线呈宽峰状，随着林龄增加，日蒸腾耗水量逐渐增加，在不同水分条件下,灌水时日蒸腾耗水量：萌生苗日蒸腾耗水量大于扦插日蒸腾耗水量。     

秦皇岛装船原油管道能耗指标测算

针对秦皇岛原油装船线装船量急剧下降的情况，从分析油电消耗着手，推导出油电指标的测算公式，探讨成本与装船计划的关系。确定了维持成本运行的最低年装船量，并提出了相应降低能耗的措施。     

放牧系统的稳定性与载畜量的研究

本文研究了具有不同消费函数的放牧系统的稳定性,并确定出相应的系统的安全容纳量和最大容纳量。本文中有些结论改进并推广了Noy—Mcir于1976年得到的关于载畜量的有关结论。     

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。     

黑河干流中游地区耗水量变化的历史分析

黑河流域水资源极其匮乏 ,加之地区上的配置不均衡和中游地区水土资源的过度开发导致了黑河下游严重生态环境问题 ,已影响到我国北方广大地区生态环境质量 ,受到国家的高度重视和全社会的广泛关注。黑河水资源开发利用主要集中在中游地区 ,该区是全国重要的商品粮、蔬菜和经济作物生产基地之一 ,耗水量占流域耗水量的 85 %以上 ,是黑河的主要耗水区和径流利用区。本文利用黑河上游控制站莺落峡站和中游控制站正义峡站水文资料 ,分析了黑河中游耗水量的变化 ,结果表明 :2 0世纪 80年代以前黑河中游耗水量在 4.2 0× 10 8m3 左右 ,比较稳定 ;进入 2 0世纪 80年代以来耗水量明显增加 ,80年代耗水量比 40年代末期至 70年代增加了 2 .0× 10 8～ 2 .5×10 8m3 左右 ;90年代耗水量又比 80年代增加了 1.6 0× 10 8m3 左右。 80年代以来流域耗水量呈明显增加的趋势 ,这是以人类活动影响为主的结果     

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.