液体地膜覆盖对棉花产量与土壤环境的影响

基于棉田可持续发展的思想,研究了液体地膜覆盖对棉花产量与土壤物理性状的影响。结果表明,适量液体地膜(112.5 kg/hm²)覆盖棉花增产显著,平均增产皮棉21.7%。液体地膜覆盖棉田提高土壤温度和含水量,降低土壤容重。表明液体地膜覆盖棉田对土壤环境无不良影响,利用液体地膜覆盖棉田是一项可行的技术。     

育秧方式与机插密度对早稻机插栽培的产量影响

双季早稻机插栽培技术研究对水稻机械化栽插及配套栽培具有重要的意义,其中早稻机插育秧方式与机插密度又是该项技术中的关键点。本研究以‘中早39’为材料,对育秧播种量、育秧床土类型和栽插密度设计不同的处理,考察其对秧苗的生长、秧苗素质、机插质量及产量的影响。结果认为,播种量为110 g/盘所培育的秧苗,出苗率、成秧率、苗重/苗高比都较其他的处理高,获得优质的机插秧苗。不同床土类型育秧对比试验表明,以“基质”或“黄泥土+基质母剂”作为床土培育的秧苗个体健壮、茎基宽大、机插质量好。移栽大田后,返青活棵较快,单位面积有效分蘖多,最终获得较高的实收产量。移栽密度试验表明,行株距为30cm×14cm和24cm×17cm,即移栽密度分别为24.45万丛/hm²和23.85万丛/hm²时,穗形较大,群体协调,实收产量最高。     

塔里木灌区棉花滴灌发展与效益分析

棉花是塔里木灌区主要种植作物.通过对该灌区历年滴灌面积、常规灌溉与滴灌棉花灌溉定额、单产及单方水效益等具体数据进行分析表明,棉花实施滴灌后每公顷可节水1 200 m3,计144元;到"十一五"末,滴灌比常规灌溉可节水约1亿m3;每公顷可使籽棉增产215 kg,计1 182.5元.     

毛管间距对膜下滴灌棉花根系及植株生长的影响

在大田膜下滴灌条件下,对毛管间距影响棉花根系分布及植株生长的特点进行了试验研究。试验中滴灌毛管布置间距分别为130、90 cm;观测了棉花不同生育阶段的根系分布及单株株高、叶面积。试验结果表明,毛管间距大,导致灌水量也大,土壤易出现深层渗漏,而且土壤水分水平分布均匀性差,造成内、外行棉花长势不均匀,棉花根茎小,根长密度水平方向呈单峰抛物线分布,垂直方向14~28 cm土层处根长密度最大;而毛管间距小,土壤水分水平分布均匀,内、外行棉花长势均匀,棉花根茎大,根长密度水平方向呈双峰抛物线分布,垂直方向0~14 cm土层处根长密度最大。但2种毛管布置间距的棉花根系生长过程、株高及叶面积生长过程均符合相同的规律。试验结果为滴灌技术设计中选定土壤湿润比和毛管间距提供参考。     

膜下滴灌棉田土壤水分参数的空间变异性分析

以新疆生产建设兵团石河子国家农业科技园区的膜下滴灌棉田作为试验区,采用均匀法布点方式,测定了各个采样点0～20、20～40和40～60 cm三个不同深度土壤的含水率,利用地统计学理论分析了膜下滴灌棉田均匀布点方式下土壤含水率的空间变异规律,并利用Sufer绘制了土壤含水率的等值线图,结果表明:①膜下滴灌棉田均匀布点采样方式下,东西方向即沿膜向的半方差函数值在0～20、20～40和40～60 cm深度层的变化规律趋于一致,随着深度增加棉田的土壤含水率的空间变异强度在逐渐减小;②各方向的半方差函数值的相关性均随着深度增加棉田的土壤含水率的空间结构性更加显著;③东西方向的空间变异性随着深度的增深而减小,其他方向的空间变异性随着深度的增深而增大;东西方向即沿膜向为弱变异,其他方向20～40 cm层为强变异,0～20、40～60 cm层为中等变异。     

棉秆联合收获机的设计

随着棉花秸杆利用率的迅速增加,其联合收获机械化的需求也日益增强。本研究设计了一种棉花秸秆拔取切碎联合收获机,可一次性完成棉秆的拔出、捡拾、切碎、集箱等作业工序,提高棉花秸秆的收获效率,减少机械进田压地次数,省工省时省费用。     

棉籽油制备生物柴油的生物降解性能

以棉籽油甲酯、乙酯生物柴油及其与石化柴油形成的调和油为研究对象,采用改进的Sturm试验方法,考察了生物柴油及其调和油的生物降解特点。研究表明：在有氧的水环境中生物降解28 d,棉籽乙酯生物柴油与甲酯生物柴油生物降解率分别达99.7%和99.1%;对甲酯生物柴油而言,生物柴油体积分数为50%和20%的调和油的28 d生物降解率分别为93.8%和80.8%,而乙酯生物柴油体积分数为50%和20%的调和油的28 d生物降解率分别95.7%和81.9%,但在相同条件下,0#柴油生物降解率仅49.9%;生物柴油在调和油中体积比越大,调和油的生物降解速度越快,生物柴油对石化柴油的生物降解具有促进作用。该文对认识棉籽油生物柴油在环境中的消解规律,控制环境污染具有一定的意义。     

棉花纤维突变体胚珠发育过程中主要生化物质的积累特征

采用4种不同类型的棉花纤维突变体和一个正常对照TM-1,分析比较了胚珠发育过程中,纤维和种皮内纤维素,种仁内脂肪、可溶性糖和蛋白质等成份的积累特征及诸成份与纤维素含量的相关关系.结果表明,棉花纤维突变体的胚珠不仅生化物质动态变化有着较大的差异,而且其生化物质的组成差异也很大.纤维突变体的纤维素含量低,种仁蛋白质含量低,脂肪含量高.纤维素含量与种仁内脂肪含量呈显著或极显著正相关;与纤维可溶性糖,纤维蛋白质有显著的相关性,呈显著负相关.     

Increasing incomes of Malian cotton farmers: Is elimination of US subsidies the only solution?

In a WTO battle and the press the argument is often made that eliminating US cotton subsidies would have a large effect on the incomes and competitive position of farmers in developing countries. In Francophone West Africa cotton productivity has stagnated after rapid gains in the first two decades following independence (1960-1980). A farm model was constructed based on farmers’ definition of their decision-making framework which they use to respond to income and weather risks. With this model the effects on farmers of eliminating US subsidies are compared with various productivity increasing measures for cotton and sorghum in Dioila, Mali. Dioila is located in a representative cotton region producing 16% of the cotton in Mali. We include sorghum due to its importance for consumption and the observation of Malian farmers substituting cereals (sorghum and maize) for cotton as the returns to cotton have fallen in the 21st Century. In the farm model, the elasticity of transmission of a change in the world cotton price to the farm gate price is taken into account. The gains from eliminating US subsides are small. In contrast, the various technological alternatives including Bt cotton introduction, the use of higher fertilization levels for cotton, and the introduction of improved sorghum cultivars and moderate fertilization along with a marketing package all have substantially higher returns Even with substantial improvement in the mechanisms enabling farmers to benefit from the higher prices resulting from elimination of US subsidies, there are still much higher returns resulting from the various types of productivity increases.     

Soil water recharge in a semi-arid temperate climate of the Central U.S. Great Plains

The amount of soil water at the beginning of the growing season has a large impact on crop yields in rainfed agriculture, especially in semi-arid regions and in years with below-average rainfall in more humid climates. Robust algorithms are needed to estimate soil water storage before planting to aid crop management decisions. The main objectives of this paper are to investigate soil water recharge during the non-growing season (October 20 to May 1) in a semi-arid, temperate ecosystem in south-central Nebraska (USA) and to evaluate empirical models to estimate soil water content at the beginning of the summer-crop growing season. A database of soil water content measurements collected over 5 years at nine locations in south-central Nebraska was used to estimate available water-holding limits in the soil profile and to determine the change in available soil water during the non-growing season. Regression analysis was performed to analyze the relationship among soil water recharge, residual soil water (i.e., soil water content at the end of the previous growing season), total precipitation, and available water-holding capacity (AWHC) in the root zone to 1.5 m. Precipitation storage efficiency (PSE) was calculated as the quotient of soil water recharge and total non-growing season precipitation. Predictive models to estimate soil water content at the beginning of summer-crop growing season were derived from these analyses. A large portion of the variation in soil water recharge was explained by residual soil water and precipitation. PSE averaged 28% across site-years; low PSE values were associated with high residual soil water and/or low AWHC. Two predictive models (linear and linear-plateau) that used residual soil water, total precipitation, and AWHC as independent variables explained 75-80% of the variation in the measured soil water content at the beginning of the summer-crop growing season. These empirical models represent a new tool to estimate soil water content by planting date of summer crops. Site-management conditions such as residue amount and its architecture, tillage system, soil texture, and terrain slope are not currently accounted for in these models and would likely improve predictive capacity.