Effects of deficit irrigation with saline water on spring wheat growth and yield in arid Northwest China

Field experiments were conducted in 2008 and 2009 to study the effects of deficit irrigation with saline water on spring wheat growth and yield in an arid region of Northwest China. Nine treatments included three salinity levels s1, s2 and s3 (0.65, 3.2, and 6.1 dS/m) in combination with three water levels w1, w2 and w3 (375, 300, and 225 mm). In 2008, for most treatments, deficit irrigation showed adverse effects on wheat growth; meanwhile, the effect of saline irrigation was not apparent. In 2009, growth parameters of w1 treatments were not always optimal under saline irrigation. At 3.2 and 6.1 dS/m in 2008, the highest yield was obtained by w1 treatments, however, in 2009, the weight of 1,000 grains and wheat yield both followed the order w2 > w1 > w3. In this study, spring wheat was sensitive to water deficit, especially at the booting to grain-filling stages, but was not significantly affected by saline irrigation and the combination of the two factors. The results demonstrated that 300-mm irrigation water with a salinity of less than 3.2 dS/m is suitable for wheat fields in the study area.     

Measuring cotton water status using water-related vegetation indices at leaf and canopy levels

Drought is one of the major environmental threats in the world. In recent years, the damage from droughts to the environment and economies of some countries has been extensive, and drought monitoring has caused widespread concerns. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties, and it offers an opportunity for the quantitative assessment of drought indicators such as the vegetation water content at different levels. In this study, sites of cotton field in Shihezi, Xinjiang, Northwest China were sampled. Four classical water content parameters, namely the leaf equivalent water thickness (EWT leaf ), the fuel moisture content (FMC), the canopy equivalent water thickness (EWT canopy ) and vegetation water content (VWC) were evaluated against seven widely-used water-related vegetation indices, namely the NDII (normalized difference infrared index), NDWI 2130 (normalized difference water index), NDVI (normalized difference vegetation index), MSI (moisture stress index), SRWI (simple ratio water index), NDWI 1240 (normalized difference water index) and WI (water index), respectively. The results proved that the relationships between the water-related vegetation indices and EWT leaf were much better than that with FMC, and the relationships between vegetation indices and EWT canopy were better than that with VWC. Furthermore, comparing the significance of all seven water-related vegetation in- dices, WI and NDII proved to be the best candidates for EWT detecting at leaf and canopy levels, with R 2 of 0.262 and 0.306 for EWT leaf-WI and EWT canopy -NDII linear models, respectively. Besides, the prediction power of linear regression technique (LR) and artificial neural network (ANN) were compared using calibration and validation dataset, respectively. The results indicated that the performance of ANN as a predictive tool for water status measuring was as good as LR. The study should further our understanding of the relationships between water-related vegetation indices and water parameters.     

Simple prediction of crude protein content in vegetation growing on abandoned cultivated land

The accuracy of two simple methods was compared for the prediction of crude protein (CP) content of above‐ground plant material of mixed‐species composition on abandoned cultivated land in Japan. The first method is based on standard CP values (in g kg^?1 dry matter) for individual species (STV method) as listed in the literature. The second procedure (GLM method) was an application of the generalized linear model using the relative above‐ground biomass of monocots and legumes, total herbage mass, and day of year. Predictions were made at the quadrat scale, and for surveyed sites based on average of values for five or six quadrats in a single survey. A ‘leave‐site‐out’ method was adopted for model validation of the generalized linear model. The observed values of CP content ranged between 21·5 and 161·9 g kg^?1 dry matter (DM). With the STV method, the values of root mean square error (RMSE indicates average estimation error) were 50·9 at the quadrat level and 53·8 at the surveyed‐site level (both g kg^?1 DM). When a ‘leave‐site‐out’ validation was carried out, the RMSE‐values for the GLM method were 23·2 at the quadrat level and 13·2 at the surveyed‐site level (both g kg^?1 DM). We therefore propose adoption of the GLM method for the purpose of estimating the CP content in herbage on abandoned sites.     

两种生境下狗牙根叶表皮亚显微结构的比较

通过对生长在田园土和植被砼2种生境下的5个品种狗牙根及野生狗牙根叶表皮微形态进行扫描电镜观察,比较了在不同生境下狗牙根叶表皮上气孔形状、大小、密度和表皮毛、乳突等附属物。结果表明,供试的5个狗牙根品种及野生狗牙根对植被砼环境作出了积极的响应与适应,上下表皮乳突明显较田园土的增多,并且有些狗牙根品种叶表皮的气孔器周围乳突有规律的排列,使气孔器凹陷在其周边的细胞里面,使气体扩散阻力增大,有利于降低蒸腾,增加植物对水分的利用效率。与田园土上生长狗牙根相比,植被砼上狗牙根上、下表皮气孔密度显著增高的有喀什狗牙根、Sahara、Heaven、Hawk。就植被砼上不同狗牙根气孔特征来说,气孔密度较其他品种显著多的品种有喀什狗牙根、Sahara。在供试的5个狗牙根品种及野生狗牙根中,根据表皮毛及其他附属物的形态特征,气孔参数的特征等综合考虑,认为Sahara、Hawk、喀什狗牙根等对植被砼生境有很好的适应性。     

基于3S技术的甘南草地覆盖度动态变化研究

以像元二分法模型为基础,构建了基于改进归一化植被指数(NDVI)的植被覆盖度定量估算模型,并利用MODIS卫星遥感数据,基于3S技术(GIS,RS,GPS)空间分析功能,分级计算得到了甘南2002,2004,2006,2008年的草地植被覆盖度,分析了甘南2002-2008年植被覆盖度变化的空间动态演变过程和趋势。结果表明,2002-2008年,甘南植被覆盖度质量总体呈下降趋势,一级盖度植被的退化比较严重,草地植被的演变情况主要由优等植被覆盖(一、二级)向低等植被覆盖(四、五级)演变。研究结果揭示了甘南州草地退化状况日趋严重的事实,为相关研究和政府草地管理提供了有效依据。     

基于GIS平台的地下水埋深与植被盖度响应关系研究

由于我国北方地区多为干旱半干旱气候,降雨稀少、蒸发强烈、生态环境脆弱,因此地下水的水位埋深对区内的植被生态有着重要的影响。本次研究以ARCGIS空间分析技术为平台,以我国通辽地区为典型研究对象,在充分掌握区内地质、水文地质以及生态植被等条件的基础上,从区域尺度上定量分析了植被生长状况与地下水位埋深的相互关系。研究表明:在内蒙古通辽地区,适宜植被生长的最佳地下水埋深为2～2.4m,植被能够生长的地下水埋深范围是1.3～3.8m,不适宜植被生长的地下水埋深范围是3.8～7.5m,当地下水埋深大于7.5m时,地下水埋深与植被覆盖度相关度很小。     

冬小麦冠层高光谱特征与覆盖度相关性研究

在2010年度与2011年度冬小麦生长季大田试验小区,实测了3个播种密度、3个灌水水平下冬小麦冠层的高光谱反射率与覆盖度。分析了冬小麦冠层光谱特征以及不同生育期冬小麦冠层光谱特征参数与覆盖度的相关性,建立了基于光谱特征参数的不同生育期冬小麦覆盖度估算模型。结果表明:从返青到成熟,冬小麦冠层可见光区光谱反射率先减小后增大,近红外区先增大后减小。不同种植密度下,适宜供水冬小麦在可见光波段的反射率依次小于轻度亏水、重度亏水条件下的冬小麦;在近红外波段,规律正好相反。在相关性分析中,传统光谱特征参数和新光谱特征参数与覆盖度在不同生育期均具有较好的相关性。相比以传统光谱特征参数为自变量的冬小麦覆盖度估算模型,基于绿峰峰度的估算模型可以提高冬小麦覆盖度的估算精度。     

基于MODIS数据的冬小麦种植面积快速提取与长势监测

利用MODIS-NDVI数据,以中国冬小麦主产区为例,探讨了基于遥感影像全覆盖的大尺度冬小麦种植面积遥感综合自动识别及长势监测的方法。通过分析冬小麦的种植结构、物候历特征及其生物学特性和时序NDVI曲线特征,确定了冬小麦信息提取的NDVI阈值,建立了冬小麦面积提取模型,并最终获取了2010—2011年中国农情遥感监测中冬小麦长势监测所需的空间分布数据,与多年平均统计数据比较,总体精度达到81%以上。基于提取的冬小麦面积信息空间分布数据,利用MODIS-NDVI差值模型,对冬小麦2011年的长势进行监测。结果表明,与近5年平均状况对比,2011年冬小麦在其整个生育期内长势基本与常年持平,但时空分布差异较大。     

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