辽宁地区玉米耗水量与产量的关系

为探讨生长季自然降水条件下水分状况对玉米产量的影响,利用锦州2011、2012年和2014年玉米分期播种试验数据和1981—2010年辽宁农业气象历史资料,研究干旱年份水分与玉米产量的定量关系。结果表明:玉米产量和总耗水量间呈二次抛物线的关系(R~2=0.793,F=105,P=0.0001,n=57),当耗水量达到400~600 mm时,玉米能够获得高产;玉米不同生育阶段需水量呈现抽雄~乳熟期拔节~抽雄期出苗~拔节期乳熟~成熟期播种~出苗期的变化规律,各阶段日耗水强度分别为5.75、4.78、2.36、1.84 mm·d~(-1)和1.35 mm·d~(-1)。在分析相对叶面积指数和相对作物系数关系的基础上,提出了作物水分胁迫系数的相对叶面积订正方法,建立了以Jenson模型为基础的玉米水分生产函数模型。     

贵德县梨树始花期与气象因子的相关分析及预报模型

为准确预报贵德地区梨树的始花期提供方法支持。笔者利用2007—2014 年青海省贵德县气象局观测的梨树始花期资料和地面气象观测资料,对影响梨树始花期的气象因子进行分析。结果表明：影响梨树始花期的主要气象因子是气温稳定通过5.0℃到3 月底的积温值。利用逐步回归分析方法建立了基于主要气象因子的梨树始花期预报模型,用所建立的预测模型对2007—2014 年梨花始花期进行回测,准确率较高。对2015 年梨树始花期日期进行预测,预测值与实际观测值之间相差1 天,预报值基本吻合。所建立的预测模型能准确预测贵德县梨树始花期的预报。     

A nonlinear,low data requirement model for producing spatially explicit fishery forecasts

Spatial variability can confound accurate estimates of catch per unit effort (CPUE), especially in highly migratory species. The incorporation of spatial structure into fishery stock assessment models should ultimately improve forecasts of stock biomass. Here, we describe a nonlinear time series model for producing spatially explicit forecasts of CPUE that does not require ancillary environmental or demographic data, or specification of a model functional form. We demonstrate this method using spatially resolved (1° × 1° cells) CPUE time series of North Pacific albacore in the California Current System. The spatial model is highly significant (P < 0.00001) and outperforms two spatial null models. We then create a spatial forecast map for years beyond the range of data. Such approaches can guide spatial management of resources and provide a complement to more data‐intensive, highly parameterized population dynamics and ecosystem models currently in use.     

灌溉用水量的并联型灰色神经网络预测

该文提出了把人工神经网络和灰色预测方法结合成并联型灰色神经网络预测方法,用这种方法来预测灌溉用水量,并以预测方法有效度为优化指标求解组合模型加权系数。结果显示,灰色神经网络预测方法的平均误差为2.67%,明显低于单一的灰色预测方法和神经网络预测方法的平均误差,可以将这种组合方法应用于中长期灌溉用水量预测。     

新疆地区参考作物腾发量的灰色模型预测

该文依据新疆地区6个站的长序列逐日气象观测资料,基于Penman-Monteith公式计算了逐日参考作物腾发量（ET0）,并应用重标极差法对ET0未来变化趋势进行了分析。运用灰色关联理论计算了各站各气象因子与年ET0间的灰色关联度和关联序。在此基础上,运用灰色系统理论建立灰色不等维递补GM（1,h）模型对6个站的年ET0进行了模拟预测,并与灰色GM（1,1）模型进行了比较。结果表明：各站ET0年内变化均呈抛物线型,4－9月ET0依各站顺序为：若羌＞吐鲁番＞哈密＞喀什＞和田＞伊宁;6站年ET0赫斯特指数均大于0.5,各站未来的趋势与历史呈正相关,依然是波动递减;总体上,平均温度、日照时数、饱和气压差对各站年ET0的影响比较大;灰色不等维递补GM（1,h）模型预测相对误差限为0～7.31%,预测精度明显高于GM（1,1）模型。该研究表明采用灰色模型预测新疆地区参考作物腾发量精度较好。     

Major current and future gaps of Brazilian reserves to protect Neotropical savanna birds

We tested the prediction that climate-driven changes might alter bird species composition in reserves of the Cerrado region of Brazil. First, we modelled the current distributions and the potential future projections of 38 endemic or rare bird species. We used eight modelling techniques within the BIOMOD computational framework in an ensemble-forecasting approach to reach a consensus scenario. Then we compared current and future (2046-2060) distributions under different scenarios (reserve size and bird dispersal) with the current Brazilian reserve system to assess the adequacy of protection (representation) of each species and detect gaps in their protection. Finally, to identify areas with high probability of occurrence of several species, we calculated cumulative climatic suitability of all 38 species for both current and future scenarios. None of the 38 species is covered under any current or future scenarios, revealing that the current reserve system is highly inefficient in conserving the analyzed bird species. The implementation of new reserves to cover species in current and future climate scenarios is recommended in areas in the south-eastern part of the Cerrado region and in the mountains of east Brazil. Due to the already high land use of the southeast region of the Cerrado, the application of non-traditional conservation measures should be evaluated.     

Traditional weather forecasting for sustainable agroforestry practices in Ilocos Norte Province,Philippines

People in rural communities in Ilocos Norte Province in the Philippines rely heavily on traditional knowledge, particularly for predicting weather to plan and prepare their agroforestry activities as well as disaster prevention. Farmers use this knowledge, derived from observations of atmospheric and astronomic conditions, indicator plants and behavior of animals (insects, birds, and mammals) which signal the onset of the rainy season. These indicators prompt farmers to prepare their upland farmlands for cropping to ensure that vegetative ground cover is established prior to heavy rainfall and thereby prevent erosion of upland soil and siltation of watercourses. Predictive knowledge of the timing of long or short rainy periods enables farmers to plant suitable crops.     

金坛地区白背飞虱的测报与防治

总结了1995年以来金坛地区白背飞虱的测报与发生情况,对田间虫卵量高的年份作了重点分析,并对白背飞虱的测报与防治药种提出了合理的建议。     

变权组合预测模型在兴平市地下水动态预测中的应用

针对地下水埋深变化离散性程度较大的兴平市,利用兴化漏斗区2000-2011年的地下水位埋深数据,采用基于指数预测法、线性回归预测法及灰色预测法的变权组合预测方法,对其进行地下水位埋深的模拟和预测。通过对兴平市地下水动态的预测,对比变权组合预测方法和单纯运用某一种单项预测方法在精度上的差别,证明了变权组合预测模型具有可行性和较高的精度,并在此基础上对兴平市未来地下水埋深进行了预测。     

日光温室揭帘时间农业天气预报指数的确定

为了提升气象服务能力,合理掌握日光温室保温帘揭盖时间,进而提升生产水平和经济效益,利用日光温室揭开保温帘时间的数学模型,对安塞冬季日光温室保温帘揭开时间进行分析验证。根据安塞县温度和光照气候资料,计算出安塞冬季各旬平均揭帘时间,并利用数学模型和天气预报,建立了揭帘农用天气预报指数,用于指导农业生产。结果表明,数学模型适合在安塞应用,天气预报指数符合生产实际,指导意义明确,按计算所得保温帘揭开时间达到了预期效果。