为全面评估区域农业水资源供需关系,基于水足迹理论构建了耕地水资源短缺指数(arable land water scarcity index,AWSI)。在分析1999-2014年中国AWSI时空分布格局的基础上,借助偏最小二乘法揭示了AWSI的主控因子。结果显示:中国AWSI的...为全面评估区域农业水资源供需关系,基于水足迹理论构建了耕地水资源短缺指数(arable land water scarcity index,AWSI)。在分析1999-2014年中国AWSI时空分布格局的基础上,借助偏最小二乘法揭示了AWSI的主控因子。结果显示:中国AWSI的年均值约为0.413,总体上处于高度水资源压力状态,且有随时间加剧的趋势;各年份AWSI以华北平原为中心向外递减式扩散;面临极高水资源压力(AWSI>0.800)的省区均分布在北方地区,长江以南省区均面临中度水资源压力(0.100<AWSI<0.400);降水量与日照时数是与耕地水短缺最为密切的气象因子,农业机械总动力、粮食面积比重以及人均GDP是影响AWSI的主要社会经济条件。农业生产水平较高的粮食主产区应以水足迹调控为重要内容进行农业水管理策略的制定。展开更多
China has the largest apple planting area and total yield in the world, and the Fuji apple is the major cultivar, accounting for more than 70％ of apple planting acreage in China. Apple qualities are affected by meteo...China has the largest apple planting area and total yield in the world, and the Fuji apple is the major cultivar, accounting for more than 70％ of apple planting acreage in China. Apple qualities are affected by meteorological conditions, soil types, nutrient content of soil, and management practices. Meteorological factors, such as light, temperature and moisture are key environmental conditions affecting apple quality that are difficult to regulate and control. This study was performed to determine the effect of meteorological factors on the qualities of Fuji apple and to provide evidence for a reasonable regional layout and planting of Fuji apple in China. Fruit samples of Fuji apple and meteorological data were investigated from 153 commercial Fuji apple orchards located in 51 counties of 11 regions in China from 2010 to 2011. Partial least-squares regression and linear programming were used to analyze the effect model and impact weight of meteorological factors on fruit quality, to determine the major meteorological factors influencing fruit quality attributes, and to establish a regression equation to optimize meteorological factors for high-quality Fuji apples. Results showed relationships between fruit quality attributes and meteorological factors among the various apple producing counties in China. The mean, minimum, and maximum temperatures from April to October had the highest positive effects on fruit qualities in model effect loadings and weights, followed by the mean annual temperature and the sunshine percentage, the temperature difference between day and night, and the total precipitation for the same period. In contrast, annual total precipitation and relative humidity from April to October had negative effects on fruit quality. The meteorological factors exhibited distinct effects on the different fruit quality attributes. Soluble solid content was affected from the high to the low row preface by annual total precipitation, the minimum temperature from April to October, the mean temperature fro展开更多
全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全...全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全氮含量的适用性。结果表明,LWR模型的预测效果优于偏最小二乘回归(PLSR)、人工神经网络(ANN)和支持向量机(SVM),选取主成分数为5,相似样本为40时,模型验证的决定系数(RP2)为0.83,均方根误差(RMSEP)为0.25 g kg-1,测定值标准偏差与标准预测误差的比值(RPD)达到2.41。LWR从建模集中选取与验证样本相似的土样作为局部建模样本,降低了差别大的样本对模型的干扰,从而提高了模型的预测能力。因此,LWR建模方法通过大范围、大样本土壤光谱数据进行大尺度区域的全氮等土壤属性预测时能够发挥更好的作用。展开更多
文摘为全面评估区域农业水资源供需关系,基于水足迹理论构建了耕地水资源短缺指数(arable land water scarcity index,AWSI)。在分析1999-2014年中国AWSI时空分布格局的基础上,借助偏最小二乘法揭示了AWSI的主控因子。结果显示:中国AWSI的年均值约为0.413,总体上处于高度水资源压力状态,且有随时间加剧的趋势;各年份AWSI以华北平原为中心向外递减式扩散;面临极高水资源压力(AWSI>0.800)的省区均分布在北方地区,长江以南省区均面临中度水资源压力(0.100<AWSI<0.400);降水量与日照时数是与耕地水短缺最为密切的气象因子,农业机械总动力、粮食面积比重以及人均GDP是影响AWSI的主要社会经济条件。农业生产水平较高的粮食主产区应以水足迹调控为重要内容进行农业水管理策略的制定。
基金supported by the Forest Scientific Research in the Public Interest,China(201404720)the earmarked fund for the China Agriculture Research System(CARS-27)the Beijing Municipal Education Commission,China(CEFF-PXM2017_014207_000043)
文摘China has the largest apple planting area and total yield in the world, and the Fuji apple is the major cultivar, accounting for more than 70％ of apple planting acreage in China. Apple qualities are affected by meteorological conditions, soil types, nutrient content of soil, and management practices. Meteorological factors, such as light, temperature and moisture are key environmental conditions affecting apple quality that are difficult to regulate and control. This study was performed to determine the effect of meteorological factors on the qualities of Fuji apple and to provide evidence for a reasonable regional layout and planting of Fuji apple in China. Fruit samples of Fuji apple and meteorological data were investigated from 153 commercial Fuji apple orchards located in 51 counties of 11 regions in China from 2010 to 2011. Partial least-squares regression and linear programming were used to analyze the effect model and impact weight of meteorological factors on fruit quality, to determine the major meteorological factors influencing fruit quality attributes, and to establish a regression equation to optimize meteorological factors for high-quality Fuji apples. Results showed relationships between fruit quality attributes and meteorological factors among the various apple producing counties in China. The mean, minimum, and maximum temperatures from April to October had the highest positive effects on fruit qualities in model effect loadings and weights, followed by the mean annual temperature and the sunshine percentage, the temperature difference between day and night, and the total precipitation for the same period. In contrast, annual total precipitation and relative humidity from April to October had negative effects on fruit quality. The meteorological factors exhibited distinct effects on the different fruit quality attributes. Soluble solid content was affected from the high to the low row preface by annual total precipitation, the minimum temperature from April to October, the mean temperature fro
文摘全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全氮含量的适用性。结果表明,LWR模型的预测效果优于偏最小二乘回归(PLSR)、人工神经网络(ANN)和支持向量机(SVM),选取主成分数为5,相似样本为40时,模型验证的决定系数(RP2)为0.83,均方根误差(RMSEP)为0.25 g kg-1,测定值标准偏差与标准预测误差的比值(RPD)达到2.41。LWR从建模集中选取与验证样本相似的土样作为局部建模样本,降低了差别大的样本对模型的干扰,从而提高了模型的预测能力。因此,LWR建模方法通过大范围、大样本土壤光谱数据进行大尺度区域的全氮等土壤属性预测时能够发挥更好的作用。
