关键词： 兴安盟地区   气候变化特征   农业   影响  

摘要： 本文通过对1971～2017年兴安盟地区的气温、降水和日照时数观测资料,分析了兴安盟地区近47年来的气候变化特征并探讨其对农作物的影响。结果表明:近47年来兴安盟地区气温主要呈现出上升变化趋势,气候倾向率为0.344℃/10a,达到了显著性检验水平,历年均值为2.8℃;春、夏、秋、冬四季平均气温变化同年变化趋势保持一致,均呈增暖趋势;降水量大致上表现为波动减少趋势,气候倾向率为0.808mm/10a,未达到显著性检验水平;气候变化的不断变暖,降水量的略微减少均会对各类农作物的正常生长发育以及作物病虫害带来不同程度的影响。

关键词： 气候变化   农业   中国气象局  

摘要： 说到气候变化,今年让全国人民感触颇多.那么,气候变化对我国农业将会带来哪些影响？该如何应对？笔者为此采访了中国气象局国家气候中心气候服务首席专家周兵.

关键词： biomass   carbon   erosion   Olea europaea   soil respiration   soil water  

摘要： Agriculture might serve as a mitigation solution through carbon (C) sequestration in soil, in tree biomass and reducing greenhouse gas (GHG) emissions. Increased C is beneficial for some soil structures and functions, improving the use of water and in turn the crop adaptation. This study reports on the synergy between mitigation and adaptation in agriculture through the paradigm of the olive (Olea europaea). Through data on net ecosystem productivity and soil respiration, the role of olive groves to store C in tree biomass (from 0.36 to 2.78 t CO2 ha(-1) yr(-1)) and into soil (similar to 8.5 t CO(2 )ha(-1) yr(-1)) is reviewed. The influence of some management practices on that role is also discussed. The overall climatic impact of olive fruit and oil production has been evaluated also considering GHG emissions by field operations (e.g., pruning, mulching of cover crop, fertilization, harvest, etc.) and by the extraction and bottling of oil. Soil C as interface between climate change mitigation and adaptation has been delineated, linking C-induced improvements in soil properties to increased water storage and reduced run-off and erosion. The outcomes may strengthen the environmental role of agriculture and promote synergistic mitigation and adaptation policies assisting in soil and water resources conservation.

关键词： Interplay management   REDD   agriculture   climate smart agriculture   climate change   integration   framing   Integrative Governance  

摘要： This article contributes to the debate on Integrative Governance by studying integration in the global forest-agriculture-climate change nexus. Since the 1990s, the role of the land-use sector, in particular forests and agriculture, has become increasingly prominent in climate change debates due to its vulnerability and its contribution to global greenhouse gas emissions. Addressing agriculture, climate change and forest policies in an integrated way could therefore create important synergies and reduce trade-offs. This article aims to analyse the extent of integration in current global governance in the nexus of agriculture, forests and climate change, and to explain this extent of integration. Based on the analysis of secondary data, participation in key events and semi-structured interviews, this article concludes that efforts to enhance integration have taken different forms for the different pairs of domains (climate change-agriculture, agriculture-forest, forest-climate change) as well as for the nexus of the three. Integration has been mainly enhanced through soft law, programmes and integrative approaches (e.g. landscape approach, climate smart agriculture, agroforestry). The analysis also shows that the extent of integration among the governance systems has differed. Interplay management efforts on forests and climate change have been relatively successful. Agriculture and forest, and agriculture and climate have low and modest levels of integration respectively, except adaptation in agriculture, which enjoys higher integration levels. Differences in integration can be explained by the medium to high degrees of legalization and the (in)compatibility of the dominant frames present in the different governance systems. Furthermore, our results show that integration in a governance system with a high degree of legalisation, and dominated by one regime, as is the case in climate change, presents important challenges. In such cases, integration might have greater

关键词： Climate change adaptation   Mountain agriculture   Local adaptation   Hidden adaptation  

摘要： Impacts of climate change have become more and more evident and can be observed in ecosystems, societies and economies worldwide. Mountain agriculture is especially vulnerable to climate change, and adaptation seems crucial. Thus, certain adaptation activities, such as installing irrigation technology, switching to drought-resistant crop varieties or shifting planting dates, can already be observed. Despite these efforts, the barriers for climate change adaptation are still manifold and lead to adaptation gaps. One problem is that many approaches ignore non-climatic drivers, such as economic conditions or cultural aspects, which have a strong influence on farmers' decisions. In the literature, the focus is mostly on planned, "top-down" induced adaptations, where climate change is considered the most important driver. Within this study, we focus on local, "bottom-up" adaptation actions in Tyrolean mountain agriculture that may be triggered by climatic as well as by non-climatic drivers. We identify 27 adaptation practices and cluster them into six types of climate change adaptation: 'Resilience-raising products and production', 'Hidden actions by farmer organizations', 'CC motivated agronomic actions', 'CCA scientific knowledge production', 'Risk-driven adaptations' and 'Hidden governmental actions'. These types are helpful to show the broad range of local practices contributing to climate change adaptation. Several adaptation actions from practice are not motivated by climate change and thus are termed "hidden" adaptations, as they do not fit into common adaptation concepts. Hidden climate change adaptation practices, although not considered to date in official CCA policy documents, constitute "low-hanging fruit" for decision makers as they have already proved their feasibility and gained legitimacy by actors on the ground. We argue that additional support for such hidden adaptation practices can help to overcome present adaptation barriers and adaptation gaps.

关键词： Climate change   Agriculture   Ricardian model   Vietnam  

摘要： This paper examines farm household-level impacts of climate change by examining the relationship between climatic variables and Vietnamese agricultural output. The Ricardian technique is applied with panel data which accounts for both adaptation strategies and household characteristics. This study proposes a two-stage Hsiao model to correct for collinearity between climatic variables and individual effects. The results show that in the dry season, increases in temperatures are beneficial to all farms in the warmer southern regions, while increases in precipitation will damage only irrigated farms in the Central and South regions. The impact of higher temperature in the wet season is similar, except that it will negatively affect net revenue of irrigated farms in the long run. More rainfall in the wet season will increase net revenue in the North region only. Finally, this study combines the estimated results with future climate scenarios to predict how future changes in climate will affect farmers on aggregate.

关键词： 政府间气候变化专门委员会   农业生产   中国   温室气体浓度   气候事件   人类活动   化石燃料   二氧化碳  

摘要： 自工业化以来,人类活动不断加剧,大量消耗化石燃料,滥砍滥伐,导致大气中二氧化碳等温室气体浓度急剧上升,造成以变暖为主要特征的气候变化。2013年政府间气候变化专门委员会（IPCC）发布的第5次评估报告显示,与19世纪下半叶相比,21世纪初期全球升温幅度为0.78℃;观测到的极端天气和气候事件发生了明显变化,高温热浪、强降水、干旱等事件频繁发生、危害日益加剧。

关键词： 气候变化   农业生产   产生影响   解决措施  

摘要： 我国幅员辽阔,地大物博,资源丰富,人口众多,农业是我国经济发展的基础产业。农业、农民和农生的"三农"问题,成为我国关注的主要问题。随着经济全球化的发展,我国同世界的联系也在不断加强,气候的变化不但影响着全球的生态平衡和经济稳定,也逐渐影响着我国的经济发展,特别是农业生产的发展。应对全球气候变化,解决气候变化带来的各种问题,成为目前世界关注的焦点。我国也进一步加强了对气候变化的分析,研究气候变化对中国农业生产的影响,本文就从气候变化的角度,分析了气候变化对我国农业生产的影响,并提出了相关的解决措施。

关键词： agriculture   machine learning   climate change impacts  

摘要： Crop yields are critically dependent on weather. A growing empirical literature models this relationship in order to project climate change impacts on the sector. We describe an approach to yield modeling that uses a semiparametric variant of a deep neural network, which can simultaneously account for complex nonlinear relationships in high-dimensional datasets, as well as known parametric structure and unobserved cross-sectional heterogeneity. Using data on corn yield from the US Midwest, we show that this approach outperforms both classical statistical methods and fully-nonparametric neural networks in predicting yields of years withheld during model training. Using scenarios from a suite of climate models, we show large negative impacts of climate change on corn yield, but less severe than impacts projected using classical statistical methods. In particular, our approach is less pessimistic in the warmest regions and the warmest scenarios.