叶龄对不挂果荔枝营养成分的影响

不挂果荔枝(Litchi chihens-is Sonn.,CV BengaⅠ)叶片中 N、K、Zn 的浓度一般随叶龄(2—25周)的增大而下降,而 Ca、Mg、Na、Cl、Fe、Mn、B则升高,N 和 Cu 的浓度随施肥水平而变,测定单项养分,依特定的养分可采取嫩叶或老叶,但同时测定多种养分,则取新梢的老熟叶片,或前一次梢的第一、二张叶片较为合适:荔枝低产通常可以归因子养分的不足(Menzel 和 Simpson,1987)。尽管叶片分析已广泛应用于研究果树的营养问题(Shear 和 Faust,1980),但对于荔枝叶片制定的标准只是根据有限的试验(Menz-el 和 Slmpson,1987)。要使叶片分析可靠地用于诊断营养状况,几种因素必须考虑,即叶龄、叶位、品种、气候、季节、病虫害和时间等(Robson,1981)。生理年龄可能是影响植物矿质养分的最重要因素(Bates,1971)。作物负荷、土壤类型、气候或栽培措施基本上不会改变养分浓度与叶龄之间的关系,但施肥水平却可能使树体营养纵向位移,以叶片反映树体营养状况的灵敏性还受到生理叶龄的影响(Guardiola,1974)。对于柑桔类,当叶龄为3—4月时,叶片含氮量与产量之间具有最好的相关性。本文研究了与生理叶龄(从出叶开始)有关的荔枝养分的变化,以及建立标准采样程序的重要资料。     

不同N、K用量对苹果产量与品质的影响

通过9年田间试验,对幼龄M26砧Aroma苹果树施用0、60及120公斤N(硝酸钙)/公顷和 0、120及240公斤K(氯化钾)/公顷,植株5年生,试验初期便结果.砂壤土,含有机质10.5%.在除草剂处理     

对阿巴拉契亚地区C_3和C_4牧草土壤水分消耗分析

改变植被类型不仅可使土壤水分利用方式发生变化,而且还能改变一个区域的水分平衡。本项研究旨在确定不同丘陵地牧场的C_3和C_4牧草对土壤含水量的影响。在中部的阿巴拉契亚地区,分别在海拔920m和615m处进行了土壤水分变化的监测。试验小区播种了鸡脚草(C_3)和柳枝稷及狗牙根(C_4)三种牧草。在920m处,鸡脚草的土壤水分消耗量比柳枝稷或狗牙根常常大20~40mm;而在615m处,它们之间不存在显著差异。此外,虽熊C_4的两种草在草冠高度和形状等方面有很大差异,但在920m和615m处柳枝稷和狗牙根之间不存在土壤水分消耗的差异。615m处各种牧草土壤水分消耗量比920m处大,这主要是受土壤性质不同的影响。     

水分亏缺和钾对拉维林娜脐橙裂果的影响

拉维林娜是一种易于裂果(一种生理失调症)的脐橙。本研究以其酸橙砧的10年生树为试材,每隔8—12天从树冠顶上喷灌一次,均施等量的N、P、K为基肥。试验处理设置如下:1)1987年7月15日—8月7日和1988年7月12日—8月8日间不灌溉;     

怎样驱、灭蚊虫？

怎样驱、灭蚊虫？１．用药液浸泡蚊帐，每浸泡一次，药效可达五至七个月之久，高温冷冻、日晒雨淋风吹都不影响药效。作法：①每平方米纱帐取水３０毫升（尼龙的取毫克）和１克三氯苯醚菊脂混合；②取塑料袋装好混合液，将蚊帐侵入，封口，用手轻轻反复挤压口袋，使药液浸...     

Environmental and behavioural factors affecting the prevalence of foot lameness in New Zealand dairy herds - a case-control study

A case-control study of environmental and behavioural factors influencing foot lameness was undertaken on 62 dairy herds comprising an average of 185 milking cows in Taranaki, New Zealand. Thirty two case herds were identified as having had at least 10 per cent of the cows lame during the milking season in which the herd was studied, and thirty control herds were selected on the basis that no more than 3 per cent of cows in these herds had been lame per year for at least two years immediately prior to investigation. Each herd was visited at both a morning and an afternoon milking, and 58 risk factors were measured between the time the farmer began to assemble the cows for milking and the completion of milking. Comparison of single variables between case and control herds identified 24 which showed differences (p<0.10). These variables were then subjected to stepwise multivariate logistic regression, and statistically significant variables in this analysis were used to create a tentative path diagram of possible causal web relationships between the various risk factors and the outcome variable, the lameness prevalence level. Information from a review of the published literature was used to include further variables to the 24 into the initial (or null hypothesis) path model. Logistic path analysis was then used to eliminate non-significant paths from the diagram, leaving 19 arrows joining 13 variables in the final path diagram, compared with 33 joining 20 variables in the initial version. The most influential variables in explaining variation between case and control herds were the average level of maintenance of the track and the degree of patience shown by the farmer in bringing the cows in for milking. Overall, factors associated with the movement of animals to the milking shed explained 40 per cent of the variation (deviance) with regard to the lameness prevalence level. Risk factors associated with characteristics of the milking process explain 24 per cent, and risk factors associated with characteristics of the cows in the herd explain 9.5 per cent. Recommendations are made on management changes which deserve further investigation as ways of reducing lameness problems in dairy herds.