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1.
  1. The significant shortfall in global marine protection targets is likely to continue to drive rapid growth in marine protected areas (MPAs). Systematic conservation planning to fill gaps in marine protection requires sufficient knowledge of both the distribution of biodiversity and the threats to species and ecosystems. Yet such data are lacking for much of the marine environment, creating significant challenges for planning effective marine protection.
  2. In the absence of habitat mapping data, critical environmental variables associated with species' distributions can be used to model the spatial distribution of different environments. Although this approach has been used in some jurisdictions to assist MPA planners, the increased availability and resolution of spatial data now provide an opportunity to improve assessments of MPA representation.
  3. Capitalizing on advances in spatial data, this study uses a range of biological and physical environmental attributes to model the distribution of Australian marine environments. Given many Australian MPAs were implemented without knowledge of the distribution of species and benthic habitats, this Bio‐physical model is used to assess MPA coverage and equality of protection for Australian marine environments.
  4. Results of the Bio‐physical model revealed that Australian MPAs overrepresent warm, offshore waters (such as the Coral Sea) and underrepresent temperate environments. Furthermore, the distribution of protection in Australian MPAs is heavily skewed, with no‐take protection disproportionately targeting tropical environments, leaving major gaps in the protection of both temperate and nearshore habitats.
  5. Without comprehensive habitat mapping, the representativeness and adequacy of an MPA system cannot be accurately evaluated, nor can the required expansion of MPAs be planned effectively. In the interim, the biological and physical attributes chosen for this model provide useful proxies to assist in efforts to better target current and future protection based on the most up‐to‐date knowledge.
  相似文献   
2.
刘景芝 《安徽农业科学》2000,28(5):647-647,649
用生物肥料——大民钾王对玉米进行拌种栽培试验 ,研究其增产效应 ,结果表明 :玉米果穗的长度、穗行数、单穗粒数、千粒重及籽粒产量均增加。其生理原因是由于大民钾王所含解钾菌能将土壤中的矿物钾、固定钾分解活化为有效钾 ,被玉米吸收利用 ,促进其根系发育 ,苗全秆壮 ,增强抗倒伏性能 ,同时增强植株光合作用 ,促进碳水化合物的形成和积累 ,增加产量  相似文献   
3.
我国进入市场经济体制之后,必然促进畜牧业产业化的快速发展。畜牧业产业化要求技术含量高,需要高新技术的投入,而畜牧业的高科技投入,实质上是现代工程技术的投入。因此,畜牧业产业化先要解决生物技术的工程化,即以先进的工程技术装备养殖业并实现养殖业产品的商品化。养殖生物技术的工程化,必须做好生产工艺、材质材料和工程技术配套的工作。  相似文献   
4.
美国红鱼养殖种群的同工酶酶谱及其生化遗传初步分析   总被引:6,自引:0,他引:6  
利用水平淀粉凝胶电泳技术,对50尾美国红鱼肌肉及肝脏组织进行了14种同工酶的初步分析。结果表明,美国红鱼多态位点比例P=0.292,实际平均杂合度Hp=0.057,预期杂合度He=0.108,每个位点的有效等位基因数Ae=1.108,Hardy-Weinberg遗传偏离指数D=-0.472。  相似文献   
5.
苎麻施用生物肥的效果   总被引:1,自引:1,他引:0  
通过苎麻施用生物肥的对比试验和生产示范,结果表明,生物肥能促进其生长势变旺:表现为叶色变绿、叶片增大、增厚。抗性增强:其抗寒性、抗风性、抗倒性分别增加36.4、12.5、2.21个百分点,其抗苎麻花叶皱缩病也明显增强。经济性状和产量、品质提高:株高、茎粗、皮厚、每公顷有效株、纤维产量和单维支数分别提高12.3cm、0.07cm、0.09mm、2422.5株、14.6%-18.6%和53公支。经济效益可观:每投入1元可增收2.27元,每公顷增加纯利816元。施用方法是:当苎麻出齐苗后每公顷施用生物肥45公斤,兑猪尿水一同泼蔸。  相似文献   
6.
Bio—G除臭剂对蛋鸡舍内氨气及产蛋性能的影响   总被引:2,自引:0,他引:2  
在面积相等,饲养密度相近的2栋蛋鸡舍内连续15d喷洒Bio-G除臭剂,每天1次,每次5L,结果表明,喷洒Bio-G后,鸡舍氨气含量明显下降,但对蛋鸡产蛋性能无明显影响。  相似文献   
7.
秦岭松栎林带生物量及其营养元素分布特征   总被引:24,自引:2,他引:22  
秦岭脱齿栎林、油松林和华山林(包括0-60cm土层)营养元素总贮量达29.2500-390.1739t.hm^-2,其大小为锐齿栎林>华山松>油松林,土壤中营养现贮量占系统总量的93.10%-99.39%,植被层和凋落物层仅占0.57%-3.00%和0.10%-1.99%;3林分乔木层生物量、木材蓄积量和营养元素积累量平均值分别为120.483t.hm^-2,129.384m^3.hm^-2和1226.9kg.hm^-2,锐齿栎林>油松林>华山松林。3林分林下植被层生物量现存量和营养元素积累量平均值分别为1.553t.hm^-2和53.1kg.hm^-2,锐齿栎林最大,华山松林最小。林下凋落物现存量和营养元素积量平均值分别为17.475t.hm^-2和502.5kg.hm^-2,3林分凋落物现存量、营养元素含量及其积累量都存在明显的差异。锐齿栎林、油松林和华山松林营养元素年吸收量、归还量、存留量和平均归还率分别为33.4、147.2和264.3,195.6、66.9和84.1,138.8、80.4和180.2kg.hm^-2和0.585、0454和0.318,3林分各营养元素的年吸收量,归还量和存留量也存在一定的差异,锐齿栎林干和皮中Ca和Mg含量远远高于油松林和华山松林的,锐齿栎林多代连作势必导致营养元素的大量流失,特别是Ca和Mg,会破坏土壤原有离子动态平衡,引起林地土壤理化性质改变,林地生产潜力逐渐下降。  相似文献   
8.
随着农业生物工程技术在农业生产中的应用,从90年代到下一个世纪农业将会发生一次新的“绿色革命”。本文简述了随着农业生物工程技术的发展和应用,将可能对农业机械的研究与开发以及农业机械化的发展产生的影响。  相似文献   
9.
植物病害生物防治与微生态学   总被引:11,自引:0,他引:11  
在概述植物病害生物防治生态学意义的基础上,从植物微生态学的角度阐述了植物根围菌及内生菌作为生防菌的依据和意义。特别是内生菌是植物体内的正常菌群,对植物病害生物防治具有重要的生态学实践意义。  相似文献   
10.
In the domain of decision‐support tools for the management of marine fish resources, considerable attention has been paid to the development of models explaining how fish stocks change over space and time. In most models, fishing effort is assumed to be exogenous and determined by factors such as management. Increasingly, there has been a call for bio‐economic models to also account for the dynamics of fishing fleets, recognizing that fishers respond to changing environmental, institutional and economic conditions. A growing literature has sought to explicitly model the endogenous determinants of the capacity of fishing fleets, the intensity of its use and its temporal and spatial allocation across fishing opportunities. We review this literature, focusing on empirical applications of the behavioural models that have been put forward to explain and predict observed fleet dynamics. We find that although economic factors are usually included as a dominant driver in most studies, this is often based on the use of proxy variables for the key economic drivers, for which adequate data are lacking. Also, while many studies acknowledge that social and social–psychological factors play a significant role in explaining observed fishing behaviour, their inclusion in fishing fleet dynamic models is still very limited. Progress in this domain can only be achieved via the development of multidisciplinary research programmes focusing on applied quantitative analysis of the drivers of fishing fleet dynamics.  相似文献   
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