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。     

内蒙古高原紫花苜蓿土壤氮素丰缺指标和推荐施氮量

为了给内蒙古高原紫花苜蓿（Medicago sativa L.）测土施氮奠定科学基础，本研究采用“零散实验数据整合法”和“养分平衡-地力差减法”新应用公式，开展了该自然区域紫花苜蓿土壤氮素丰缺指标和推荐施氮量研究。结果表明：内蒙古高原生长第1年紫花苜蓿土壤碱解氮第1~6级丰缺指标为≥48，20~48，8~20，4~8，2~4和<2 mg·kg^-1，土壤全氮第1~5级丰缺指标为≥1.4，0.8~1.4，0.4~0.8，0.2~0.4和<0.2 g·kg^-1，土壤有机质第1~6级丰缺指标为≥17，10~17，6~10，3~6，2~3和<2 g·kg^-1。当紫花苜蓿目标产量9~18 t·hm^-2、氮肥利用率40%时，内蒙古高原紫花苜蓿第1~6级土壤推荐施氮量分别为0，68~135，135~270，203~405，270~540和338~675 kg·hm^-2。     

不同施肥处理对马铃薯农田土壤理化性状及产量的影响

为了探究不同施肥处理对马铃薯农田土壤理化性状及产量的影响,试验地采用马铃薯连作模式,设置3个施肥处理,即：单施化肥(T)、有机肥配施化肥(YTF 1/2)、全量有机肥(YTF)。结果表明：在不同施肥处理下马铃薯农田土壤的理化性质和马铃薯产量发生了变化,其中变化最为明显的土壤指标有土壤容重、孔隙度、饱和导水率、有效磷。YTF处理较T处理可分别显著(P<0.05)降低土壤容重16.8%,增加土壤孔隙度12.7%,提升饱和导水率25.3%。YTF处理可显著提升土壤有效磷含量43.0%,但各处理间土壤pH、有机碳、全氮、全磷、碱解氮、速效钾之间差异并未达到显著水平。同时,较之T处理,YTF处理亦可显著提升土壤团聚体含量。YTF和YTF 1/2处理可分别较T处理提升马铃薯产量24.6%和12.8%。因此,施用有机肥不仅可以改善土壤结构,改良土壤物理性状,亦能促产增收。     

青海省祁连圆柏天然林单木生物量模型构建

利用祁连圆柏整株生物量与生长指标数据，为估算祁连圆柏林的生物量估算提供参考。通过野外调查，共获取了63株祁连圆柏天然林样木生物量与生长指标实测数据。用其中50株样木数据进行回归模拟，用其余的13株样木数据对模型可靠性进行检验，构建器官生物量与生长指标间的回归模型。结果表明，祁连圆柏单木水平下，树干生物量模型的R²_adj为0.96，均方根、模型有效性和残差系数分别为0.50、0.85和0.05；枝条生物量模型的R²_adj为0.897，均方根、模型有效性和残差系数分别为0.69、0.80和-0.66；叶生物量模型的R²_adj为0.61，均方根、模型有效性和残差系数分别为0.54、0.86和0.15；根生物量模型的R²_adj为0.93，均方根、模型有效性和残差系数分别为0.12、0.997 和-0.01。在调查数据范围内构建的模型较好地反映了祁连圆柏生物量与生长指标间的关系，形式简单、使用方便；与实测值相比，树干与叶生物量模拟值偏小，枝和根偏大。     

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。     

Determination of critical nitrogen dilution curve based on leaf area index for winter wheat in the Guanzhong Plain,Northwest China

Excessive use of nitrogen(N) fertilizers in agricultural systems increases the cost of production and risk of environmental pollution. Therefore, determination of optimum N requirements for plant growth is necessary. Previous studies mostly established critical N dilution curves based on aboveground dry matter(DM) or leaf dry matter(LDM) and stem dry matter(SDM), to diagnose the N nutrition status of the whole plant. As these methods are time consuming, we investigated the more rapidly determined leaf area index(LAI) method to establish the critical nitrogen(N_c) dilution curve, and the curve was used to diagnose plant N status for winter wheat in Guanzhong Plain in Northwest China. Field experiments were conducted using four N fertilization levels(0, 105, 210 and 315 kg ha-1) applied to six wheat cultivars in the 2013–2014 and 2014–2015 growing seasons. LAI, DM, plant N concentration(PNC) and grain yield were determined. Data points from four cultivars were used for establishing the N_c curve and data points from the remaining two cultivars were used for validating the curve. The N_c dilution curve was validated for N-limiting and non-N-limiting growth conditions and there was good agreement between estimated and observed values. The N nutrition index(NNI) ranged from 0.41 to 1.25 and the accumulated plant N deficit(N_(and)) ranged from 60.38 to –17.92 kg ha~(-1) during the growing season. The relative grain yield was significantly affected by NNI and was adequately described with a parabolic function. The N_c curve based on LAI can be adopted as an alternative and more rapid approach to diagnose plant N status to support N fertilization decisions during the vegetative growth of winter wheat in Guanzhong Plain in Northwest China.     

不同秧龄对沿江地区双季晚稻秧苗素质及产量的影响

以早熟高产品种上农粳2号和镇稻18为材料,采用常规毯苗机械栽插,分别设置20 d、24 d、28 d、32 d等4个秧龄处理,研究不同秧龄对沿江地区双季晚稻秧苗素质、生育进程、茎蘖动态及产量构成的影响。结果表明,随秧龄天数增加,参试品种的绿叶数、茎基宽和根系盘结力均逐渐升高;与20 d、24 d和28 d秧龄的处理相比,32 d秧龄处理的成熟期提前4~5 d,茎蘖高峰期提早15 d左右,结实率显著升高;与24 d秧龄处理相比,上农粳2号和镇稻18在32 d秧龄处理下的产量分别提高10.26%和20.82%。在秧苗强化化控和苗床管理的条件下,安徽沿江地区常规毯苗机插的栽插秧龄可延长至32 d。