Impact of climate change on maize yield in China from 1979 to 2016

Climate change severely impacts agricultural production, which jeopardizes food security. China is the second largest maize producer in the world and also the largest consumer of maize. Analyzing the impact of climate change on maize yields can provide effective guidance to national and international economics and politics. Panel models are unable to determine the group-wise heteroscedasticity, cross-sectional correlation and autocorrelation of datasets, therefore we adopted the feasible generalized least square(FGLS) model to evaluate the impact of climate change on maize yields in China from 1979–2016 and got the following results:(1) During the 1979–2016 period, increases in temperature negatively impacted the maize yield of China. For every 1°C increase in temperature, the maize yield was reduced by 5.19 kg 667 m–2(1.7%). Precipitation increased only marginally during this time, and therefore its impact on the maize yield was negligible. For every 1 mm increase in precipitation, the maize yield increased by an insignificant amount of 0.043 kg 667 m–2(0.014%).(2) The impacts of climate change on maize yield differ spatially, with more significant impacts experienced in southern China. In this region, a 1°C increase in temperature resulted in a 7.49 kg 667 m–2 decrease in the maize yield, while the impact of temperature on the maize yield in northern China was insignificant. For every 1 mm increase in precipitation, the maize yield increased by 0.013 kg 667 m–2 in southern China and 0.066 kg 667 m–2 in northern China.(3) The resilience of the maize crop to climate change is strong. The marginal effect of temperature in both southern and northern China during the 1990–2016 period was smaller than that for the 1979–2016 period.     

基于距离相关系数和KNN回归模型的森林蓄积量估测研究

【目的】探究Landsat8 OLI数据和KNN算法在森林蓄积量估测中的潜力。【方法】以湖南省湘潭县为研究区,采用Landsat8 OLI数据和同时期的二类调查数据,通过距离相关系数筛选特征,分别采用线性回归模型(MLR)、K-近邻模型(KNN)、距离加权KNN模型(DW-KNN)和优化欧式KNN模型(FW-KNN)对森林蓄积量进行估测。使用十折交叉方法进行精度检验,对检验结果进行对比分析。【结果】3种KNN模型的估测结果均高于传统的线性模型,并且在3种KNN模型中,FW-KNN算法效果最好,决定系数达到0.69,为3种模型中最高;3种KNN模型中,本研究优化欧氏距离KNN模型的估测精度最高,其均方根误差为30.3%,相比于传统KNN模型的均方根误差降低了5.1%,相比于DW-KNN模型降低了3.3%。【结论】采用DW-KNN蓄积量估测结果明显优于其他两种模型,说明通过特征与蓄积量的相关性优化样本间的距离是一种可行的KNN优化方法。     

基于Copula 函数的边坡可靠度高效分析方法

将Copula函数与高效蒙特卡洛方法结合,提出了含相关随机变量的边坡可靠度高效分析方法。以两个岩质边坡稳定性问题为例验证了所提方法的有效性。结果表明,该方法相比于直接蒙特卡洛方法在保证边坡失效概率的准确性的同时计算效率更高。Copula函数可以构造出具有不同相关结构的岩土体参数的联合概率分布,与高效蒙特卡洛方法(即蒙特卡洛重要抽样方法和子集模拟)结合能高效地处理含多种相关随机变量的边坡可靠度计算问题,相比于现行的Nataf变换方法结果更能体现岩土边坡真实稳定性。此外,该方法也能高效地计算含有复杂的隐式功能函数的边坡可靠度,研究成果拓展了高效蒙特卡洛方法在边坡可靠度分析中的应用。     

数值模型结构不确定性对模拟结果的影响分析

以内蒙古鄂托克旗为例,基于GMS中的MODFLOW模块构建了地下水流数值模拟模型,分析了模型结构(含水层厚度、参数分区)与模型参数不确定性因素对模拟结果的影响.研究结果表明:含水层不确定情景(含水层下边界概化为隔水底板平均值870 m)与实际情况水头差值绝对值的累计和最大为701 m,对模拟结果起了主控作用;当含水层下边界概化为910,940 m时,累计和分别增加为1 013,1 593 m;与仅考虑单个不确定性因素相比,同时考虑模型参数与含水层不确定情景累计和最大为738 m,同时考虑参数分区与含水层不确定情景累计和最大为791 m.因此,在构建地下水流数值模拟模型时,应优先考虑含水层空间结构概化的合理程度,同时考虑多个不确定性因素对模拟结果的综合影响,使地下水数值模拟模型能更精确地反映真实的地下水流状况.     

王正非林火蔓延模型改进研究

王正非林火蔓延模型是目前国内比较先进的森林火灾蔓延模型,但是由于林火行为的复杂性,此模型在准确率和普适性方面仍存在许多不足。本文结合已有的成果进一步改进王正非模型,增加了可燃物湿度对林火蔓延速度的影响,改进了林火初始速度的计算方式。针对各种坡度对林火蔓延行为的影响给出了一个计算公式。与改进前相比,改进后的模型使其对林火蔓延情况的预估更为精准。     

体外产气法评价全株藜麦与全株玉米混合青贮饲料饲用价值

为提高全株藜麦的饲料化利用率，寻找其与全株玉米的最佳青贮比，试验共设全株藜麦与全株玉米比为100：0，90：10，80：20等11个组合进行青贮发酵，发酵60 d后测定各组青贮料的体外产气发酵指标，使用灰色关联度分析筛选出最佳青贮比例。结果表明，体外发酵至36 h时，各处理组GP趋于稳定，体外发酵减弱接近停止；全株藜麦与全株玉米比例为90：10时，该青贮饲料组合的粗蛋白（CP）和粗脂肪（EE）含量显著高于其他各组（P<0.05），乙酸（AA）、总挥发性脂肪酸（TVFA）含量以及氨态氮（NH₃-N）浓度显著高于其他各处理（P<0.05）。分析表明，全株藜麦与全株玉米比例为90：10时，混合青贮饲料的营养品质和产气发酵特性综合表现最好，可作为优质混合青贮饲料在家畜养殖中推广使用。     

Cohort‐specific variation in juvenile coho salmon habitat use

In this study, we examined summer and fall freshwater rearing habitat use by juvenile coho salmon (Oncorhynchus kisutch) in the quickly urbanising Big Lake drainage in south‐central Alaska. Habitat use was assessed by regressing fish count data against habitat survey information across thirty study sites using generalised linear mixed models. Habitat associations were examined by age‐0 and age‐1+ cohorts separately, providing an opportunity to compare habitat use across different juvenile coho salmon life stages during freshwater rearing. Regression results indicated that the age‐0 cohorts were strongly associated with shallow, wide stream reaches with in‐stream vegetation, whereas age‐1+ cohorts were associated with deeper stream reaches. Furthermore, associations between fork length and habitat characteristics suggest cohort‐specific habitat use patterns are distinct from those attributable to fish size. Habitat use information generated from this study is being used to guide optimal fish passage restoration planning in the Big Lake drainage. Evidence for habitat use partitioning by age cohort during freshwater juvenile rearing indicates that pooling age cohorts into a single “juvenile” stage for the purposes of watershed management may mask important habitat use dynamics.     

同源四倍体及其原二倍体黑皮冬瓜光合光响应模型筛选

为了筛选出最适合黑皮冬瓜Benincasa hispida (Thunb.) Cogn.的光合光响应模型,为其育种提供理论依据,以同源四倍体及其原二倍体黑皮冬瓜为试验材料,对8种经典的光合光响应模型适用性进行了比较分析。结果表明：二次多项式能够表现出光抑制情况,但在拟合过程中出现暗呼吸速率为正值、光补偿点为负值及无法解释当光强达到饱和后光合速率快速下降的问题;直角双曲线、非直角双曲线及指数函数Ⅰ、指数函数Ⅱ无法直接求取光饱和点、光补偿点,结合常用的光饱和点的计算方法得到的光饱和点与实测值均存在较大的偏差,且指数函数Ⅱ在计算光饱和点时表现出明显的人为性,也无法拟合光抑制情况,但4种模型拟合得到的光补偿点均与实测值相差不大;指数修正模型因系数β为负值,无法求取四倍体黑皮冬瓜材料的光饱和点和最大净光合速率,且拟合得到的四倍体黑皮冬瓜的光补偿点明显低于实测值;直角双曲线修正模型计算得到的暗呼吸速率及二倍体黑皮冬瓜的光饱和点明显低于实测值,但获得的四倍体及其二倍体的最大净光合速率与实测值最接近,说明其在拟合最大净光合速率上有优势;整体上分段函数计算得到的黑皮冬瓜的各光合参数与实测值最为接近,与实测值的平均相对误差最小,也能很好的拟合发生光抑制部分的光响应曲线。分段函数拟合同源四倍体及其原二倍体黑皮冬瓜光合光响应曲线效果较其他模型效果好,分段函数模型为黑皮冬瓜最适合的光合光响应模型。     

中国粮食进口安全综合评价研究——基于超效率DEA模型

在对粮食及粮食进口安全内涵加以界定的基础上,从粮食数量、粮食质量、粮食价格、粮食贸易、国家经济、生态环境6个方面分析中国粮食进口安全的影响因素,基于超效率DEA模型构建中国粮食进口安全评价模型。对1992—2015年中国粮食进口安全状况进行评价,结果表明,1992—2015年中国粮食进口处于安全状态,粮食进口对我国粮食价格安全和生态安全的影响程度较小,对数量安全、质量安全、贸易安全和经济安全的影响程度较大。