近20年气候变暖对黑龙江省水稻增产的贡献

研究过去的气候变暖对作物产量的影响是认识变暖对农业影响的有效途径,但如何定量区分出气候变化和技术发展对粮食作物的影响程度是目前研究的难点。本文提出的算法在一定程度上可以克服传统算法缺陷,用此方法估算过去20年气候变暖对黑龙江省水稻单产增加的贡献率,结果表明：20世纪80年代相对于70年代水稻单产增加了30.6%,其中由气候变暖带来的增产量占实际增产量的12.8%~16.1%,相当于使70年代的单产增加3.9%~4.9%。20世纪90年代水稻单产较80年代增产42.7%,其中,气候变暖对单产增加的贡献率约为23.2%~28.8%,相当于在20世纪80年代的单产水平上增产9.9%~12.3%。     

硅肥对水稻镉吸收影响初探

试验表明：用炼铁水淬渣为主要原料生产的硅肥,能够抑制水稻对镉吸收。在本试验条件下,施硅肥后糙米镉含量相对下降９２％以上,稻草镉含量下降９４％。随着硅肥施用量的增加,抑制作用有增强的趋势,作者认为在镉污染不太严重的地区,施用硅肥可以防止重金属污染。     

融合空间和时序遥感信息的深度学习水稻提取

为了得到更加精细的水稻提取结果,提出一种结合高分辨率和多时序遥感影像的深度学习水稻提取方法.构建全卷积网络(FCN)对BJ-2高分辨率遥感影像进行分类,并利用长短期记忆网络(LSTM)和随机森林(RF)分类器对Sentinel-2多时序遥感影像进行分类,再通过面向对象的分割和投票对3种方法的分类结果进行融合,得到最终提...     

成都平原区水稻土成土剖面Cd形态分布特征及影响因素研究

成土过程中不同形态镉(Cd)迁移转化的影响因素研究对于了解Cd的地球化学行为具有非常重要的意义。以四川省成都平原区农田生态系统水稻土剖面中不同形态Cd分布特征为例,探讨成土过程中不同形态Cd的迁移转化影响因素及其生态危害性。结果表明:总量Cd、离子交换态Cd和碳酸盐Cd均表现出表层富集,深层含量稳定的特征;岷江流域和沱江流域水稻土Cd含量的背景值分别为0.147,0.215mg·kg-1。外源输入耕层土壤中Cd主要为活动态,其增加量占Cd总量增加量的60.71%~90.52%,向下垂向迁移能力非常弱。成土过程随着土壤中Cd总量增加,稳定态Cd含量显著增加,而活动态Cd含量明显降低。土壤的pH值主要对碳酸盐态Cd含量有影响,pH值越大,其含量越高;随着土壤成熟度增高,粉粒和粘粒含量增加的同时,残渣态Cd含量显著降低,而有机结合态Cd含量显著增高。耕层土壤中Cd的生物有效性系数高达0.59~0.65,已经严重地影响到农作物安全。     

水稻土中重金属元素Cd、Pb的竞争吸附——以长株潭地区水稻土为例

对中国南方的3个水稻土样品进行了Cd、Pb单一离子和混合离子吸附特点的研究。结果表明,3个水稻土样品中Cd、Pb的吸附特点都相似,Langmuir等温方程可很好地描述Cd、Pb的吸附等温线。3个土壤样品中,有较高pH值和较低有机物、CEC浓度、粘土含量、高岭石含量的2个土壤样品对Cd、Pb有较大的吸附量,且其等温吸附拟合的最大吸附量（B）也较大。在两组分混合溶液中,尽管共存离子的存在影响了土壤对单一离子的吸附,同时土壤对Cd的吸附在一定程度上受影响的程度大,但3个土壤样品都表现出对Pb有强的吸附能力。在3个土壤样品中,Langmuir等温方程中Pb的健合能常数（K）都大于Cd的健合能常数（K）,混合溶液中的K值高于单一溶液的K值,表明2种金属离子对吸附位点的竞争提高了特定吸附位点的保持力,使金属离子在土壤中特定位点的吸附更加坚固。     

3S技术在晚稻长势监测中的综合应用研究

以江西省为试验区，探讨用RS－GIS－GPS技术监测作物长势的方法和途径。针对丘陵山区地形复杂、地块不成规模的情况，在GIS支持下，将数字高程数据、土地利用分类数据与GPS定位观测数据相结合，对NOAA／AVHRR进行判读，提取反映双季晚稻长势的植被指数，用该指数与GPS定位观测资料及地理信息数据进行监督分类监测苗情，并用迭代自组织分析法进行无GPS定位观测资料时相应时相的NOAA／AVHRR的长势监测。研究结果表明，选用的地理信息数据（数字高程数据、土地利用分类）与GPS定位观测资料相结合提高NOAA／AVHRR数据的利用效果是适合的和切实可行的。     

影响我国水稻产量的主要气象因子的研究

通过收集前人所作的全国各地区水稻产量气象预报模型,将所得到的资料按双季早稻、双季晚稻、单季稻进行分析,并提取预报因子。通过定量化处理,使用系统聚类分析方法,以预报方程中的影响因子为指标,讨论了各水稻分区（双季早稻分为４个区,双季晚稻分为２个区,单季稻分为９个区）水稻产量的主、次要影响因子和影响时期,为大范围水稻产量预报提供了科学依据。     

Comparison of manual and automatic methods for measurement of methane emission from rice paddy fields

The methane emission flux from rice paddies was simultaneously measured with automatic and manual methods in the suburban of Suzhou. Both methods were based on the static chamber/ GC-FID techniques. Detail analysis of the experimental results indicates: a) The data of methane emission measured with the automatic method is reliable. b) About 11 or 19 o’clock of local time is recommended as the optimum sampling time for the manual spot measurement of methane emission from rice paddies. The methane emission fluxes measured by manual sampling at local time other than the optimum time have to be corrected. The correction coefficient may be determined by automatic and continuous measurement. c) In order to get a more accurate result, an empirical correction factor, such as 18%, is recommended to correct the seasonally total amount of measured methane emission by enlarging the au-tomatically measured data or reducing the manually measured ones.     

A meta-analysis of global crop water productivity of three leading world crops (wheat,corn, and rice) in the irrigated areas over three decades

ABSTRACT

The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world’s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered from published articles spanning 31 different countries. There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets. For example, corn grown in latitude 40–50° had much higher mean CWP (2.45?kg/m³) compared to mean CWP of corn grown in other latitudes such as 30–40° (1.67?kg/m³) or 20–30° (0.94?kg/m³). The same trend existed for wheat and rice as well. For soils, none of the CWP values, for any of the three crops, were statistically different. However, mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes. This applied for all three crops studied. For wheat, the global CWP categories were low (≤0.75?kg/m³), medium (>0.75 to <1.10?kg/m³), and high CWP (≥1.10?kg/m³). For corn the global CWP categories were low (≤1.25?kg/m³), medium (>1.25 to ≤1.75?kg/m³), and high (>1.75?kg/m³). For rice the global CWP categories were low (≤0.70?kg/m³), medium (>0.70 to ≤1.25?kg/m³), and high (>1.25?kg/m³). USA and China are the only two countries that have consistently high CWP for wheat, corn, and rice. Australia and India have medium CWP for wheat and rice. India’s corn, however, has low CWP. Egypt, Turkey, Netherlands, Mexico, and Israel have high CWP for wheat. Romania, Argentina, and Hungary have high CWP for corn, and Philippines has high CWP for rice. All other countries have either low or medium CWP for all three crops. Based on data in this study, the highest consumers of water for crop production also have the most potential for water savings. These countries are USA, India, and China for wheat; USA, China, and Brazil for corn; India, China, and Pakistan for rice. For example, even just a 10% increase in CWP of wheat grown in India can save 6974 billion liters of water. This is equivalent to creating 6974 lakes each of 100?m³ in volume that leads to many benefits such as acting as ‘water banks’ for lean season, recreation, and numerous ecological services. This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%, 20%, and 30%.     

融合时间序列环境卫星数据与物候特征的水稻种植区提取

获取高精度的区域水稻种植面积对于农业规划、配置与决策具有重要意义。区域尺度的水稻面积获取依赖于高时空分辨率影像,但受卫星回访周期和气候影响,难以获取足够时间序列的高时空分辨率影像,从而影响水稻种植面积遥感提取的精度。为此,提出适应于中国南方多雨云天气地区,基于国产环境卫星(HJ-1A/1B)与MODIS融合数据的水稻种植面积提取的新方法。以洞庭湖区为实验区,利用STARFM模型融合环境卫星NDVI数据与MODIS13Q1数据,获取时间序列的环境卫星NDVI数据,利用水稻关键期的NDVI数据结合物候特征参数对水稻种植区域进行提取。结果表明,该方法能有效提取区域水稻种植的面积,水稻种植面积提取的总体精度与Kappa系数分别达到91.71%与0.9024,分类结果明显优于仅采用多光谱影像或NDVI数据。该研究为中国南方多雨云天气地区水稻种植面积提取提供了有效的方法。