Capturing market impacts of farm level policies: a statistical extrapolation approach using biophysical characteristics and farm resources

Technical change at the farm level or changes in input prices often have an impact on the firm's supply function, which in turn affect their economic and environmental performance. These changes can take place in numerous ways. This paper presents a methodology that increases the consistency of supply responses across various sets of agricultural products and most representative farm typologies in Europe, with a market model based on a statistical response function approach. Since most farm simulation models are limited to a subset of regions and farm types, the linkage to an aggregated model requires a procedure for expanding these results to non-sample regions to achieve full regional coverage. This paper addresses theoretical aspects related to the consistency between micro and market level models. The proposed approach is applied using a consistent set of simulation results from farm models in seven European regions. Our results show a fairly stable behaviour of the farm models considered for the analysis and quite good fit of the estimated response surface. As results are still preliminary we critically reflect on the applicability of this method in addressing further needs on up-scaling of other economic as well as environmental indicators.     

Exploring diversity in soil fertility management of smallholder farms in western Kenya: I. Heterogeneity at region and farm scale

The processes of nutrient depletion and soil degradation that limit productivity of smallholder African farms are spatially heterogeneous. Causes of variability in soil fertility management at different scales of analysis are both biophysical and socio-economic. Such heterogeneity is categorised in this study, which quantifies its impact on nutrient flows and soil fertility status at region and farm scales, as a first step in identifying spatial and temporal niches for targeting of soil fertility management strategies and technologies. Transects for soil profile observation, participatory rural appraisal techniques and classical soil sampling and chemical analysis were sampled across 60 farms in three sub-locations (Emuhaia, Shinyalu, Aludeka), which together represent much of the variability found in the highlands of western Kenya. Five representative farm types were identified using socio-economic information and considering production activities, household objectives and the main constraints faced by farmers. Soil fertility management and nutrient resource flows were studied for each farm type and related to differences in soil fertility status at farm scale. Farm types 1 and 2 were the wealthiest; the former relied on off-farm income and farmed small pieces of land (0.6–1.1 ha) while the latter farmed relatively large land areas (1.6–3.8 ha) mainly with cash crops. The poorest farm type 5 also farmed small pieces of land (0.4–1.0 ha) but relied on low wages derived from working for wealthier farmers. Both farm types 1 and 5 relied on off-farm earnings and sold the least amounts of farm produce to the market, though the magnitude of their cash, labour and nutrient flows was contrasting. Farms of types 3 and 4 were intermediate in size and wealth, and represented different crop production strategies for self-consumption and the market. Average grain yields fluctuated around 1 t ha⁻¹ year⁻¹ for all farm types and sub-locations. Grain production by farms of types 4 and 5 was much below annual family requirements, estimated at 170 kg person⁻¹ year⁻¹. Household wealth and production orientation affected the pattern of resource flow at farm scale. In the land-constrained farms of type 1, mineral fertilisers were often used more intensively (ca. 50 kg ha⁻¹), though with varying application rates (14–92 kg ha⁻¹). The use of animal manure in such small farms (e.g. 2.2 t year⁻¹) represented intensities of use of up to 8 t ha⁻¹, and a net accumulation of C and macronutrients brought into the farm by livestock. In farms of type 5, intensities of use of mineral and organic fertilisers ranged between 0–12 kg ha⁻¹ and 0–0.5 t ha⁻¹, respectively. A consistent trend of decreasing input use from farm types 1–5 was generally observed, but nutrient resources and land management practices (e.g. fallow) differed enormously between sub-locations. Inputs of nutrients were almost nil in Aludeka farms. Both inherent soil properties and management explained the variability found in soil fertility status. Texture explained the variation observed in soil C and related total N between sub-locations, whereas P availability varied mainly between farm types as affected by input use.     

Integrated assessment of future CAP policies: land use changes,spatial patterns and targeting

The recent and upcoming reforms of the Common Agricultural Policies (CAPs) aim at strengthening the multifunctional role of agriculture, acknowledging the differences in economic, environmental and social potentials within European regions. This paper presents results from an integrated assessment of existing and future policies within the framework set up in the FP6 EU project MEA-Scope. Spatial explicit procedures allow for the MEA-Scope modelling tools to provide information related to regional, environmental and socio-economics settings. The impact of different policy scenarios on structural change, land abandonment and cropping pattern of typical farms has been assessed based on linked agent-based (ABM) and Linear Programming (LP) models at regional and farm scale for two study areas. For the German case study area Ostprignitz-Ruppin (OPR), the issue of policy targeting has been addressed by relating non-commodity outputs (NCOs) to soil quality and protection status. For the Italian case study area (Mugello), changes in landscape patterns in terms of increased fragmentation or homogeneity as affected by changes in agricultural intensity have been analysed using semivariance analysis. The spatial explicit approach highlighted the relevance of case study research in order to identifying response structures and explaining policy implementation patterns.     

Relationships between Danish organic farming and landscape composition

This article presents an investigation of relationships between organic farming and landscape composition in Denmark. Landscape composition was analysed in terms of density of uncultivated landscape elements (I), number of land uses per hectare (II), diversity of land use (III) and mean field size (IV). Two analytical approaches were used. The first was based on an examination of the national agricultural registers for 1998, 2001 and 2004. The second approach used aerial photo interpretation for an analysis of 72 conventional and 40 organic farms within three sample areas for 1982, 1995 and 2002. The national analysis indicated that organic farming has a direct effect on landscape composition. In 2001, organic farms were characterised by a higher number of land uses per ha, a higher land use diversity and smaller mean field sizes. From 1998 to 2004, conversion to organic farming was related to an increasing number of land uses per ha, increasing land-use diversity and decreasing mean field sizes. Relationships between organic farming and landscape composition were independent of variations in regional location, farm size or farm size change. At the level of sample areas, a significant relationship between organic farming and landscape composition was only found for densities of small biotopes. However, when differences in farm size and physical geographical conditions between conventional and organic farms were taken into account, several significant differences in landscape composition were clarified in two of the three sample areas. Furthermore, changes in landscape composition following conversion to organic farming were largely biased by the characteristics of the sample areas. Thus, in contrast to the national level, the sample area study indicated that differences in landscape composition between organic and conventional farms were not a direct implication of organic farming practices, but were related to variations within other parameters and to the location of organically farmed land.     

Food security and climate change in drought-sensitive savanna zones of Ghana

Desertification, climate variability and food security are closely linked through drought, land cover changes, and climate and biological feedbacks. In Ghana, only few studies have documented these linkages. To establish this link the study provides historical and predicted climatic changes for two drought sensitive agro-ecological zones in Ghana and further determines how these changes have influenced crop production within the two zones. This objective was attained via Markov chain and Fuzzy modelling. Results from the Markov chain model point to the fact that the Guinea savanna agro-ecological zone has experienced delayed rains from 1960 to 2008 while the Sudan savanna agro-ecological zone had slightly earlier rains for the same period. Results of Fuzzy Modelling indicate that very suitable and moderately suitable croplands for millet and sorghum production are evenly distributed within the two agro-ecological zones. For Ghana to adapt to climate change and thereby achieve food security, it is important to pursue strategies such as expansion of irrigated agricultural areas, improvement of crop water productivity in rain-fed agriculture, crop improvement and specialisation, and improvement in indigenous technology. It is also important to encourage farmers in the Sudan and Guinea Savanna zones to focus on the production of cereals and legumes (e.g. sorghum, millet and soybeans) as the edaphic and climatic factors favour these crops and will give the farmers a competitive advantage. It may be necessary to consider the development of the study area as the main production and supply source of selected cereals and legumes for the entire country in order to free lands in other regions for the production of crops highly suitable for those regions on the basis of their edaphic and climatic conditions.     

从绿色贸易壁垒谈中国农产品贸易的对策思路

本文运用环境经济学的理论和方法 ,分析了 WTO中的绿色贸易壁垒从质量、成本、营销三个方面对中国农产品贸易产生的影响以及中国目前农产品贸易的形式 ,提出中国在入世后农产品贸易全球化发展中所应采取的对策和建议     

National pathways to the Sustainable Development Goals (SDGs): A comparative review of scenario modelling tools

The recently-adopted global Sustainable Development Goals (SDGs) will have significant implications for national development planning in both developed and developing countries in the post-2015 period to 2030. Integrated, nationally-owned SDG strategies will be at the centre of national efforts to implement the new sustainable development agenda. The long-run processes and systems perspective that are inherent in the SDGs present complex analytical problems for policymakers and analysts. Scenario analysis and quantitative modelling will be important analytical tools to support national sustainable development planning, and there is an increasingly sophisticated suite of models available to decision makers. This paper reviews and assesses a broad range of different quantitative models that have the potential to support national development planning for the SDGs. The study develops a typology and inventory of 80 different models, and then reviews the comparative strengths, weaknesses and general utility of different models through an initial screening and subsequent multi-criteria analysis of short-listed models. Current gaps in model capabilities are highlighted in the context of providing analytical support for national development planning for the SDGs. While some existing models are particularly relevant, it is unlikely that an ideal model can analyse all SDG targets and variables of interest within a single modelling framework. Top-down ‘macro framework’ models are likely to be more useful for undertaking system-level or economy-wide scenario analysis driven by the national long-term goals and targets, and for exploring trade-offs and synergies among sectors. Bottom-up sectoral models will be able to support far more detailed option-level impact analysis of concrete interventions, technologies and investments. Combining both approaches within an analytical framework will provide a robust approach for analysis and decision-making. The results highlight a range of potential gaps in current modelling capabilities, and provide some new tools to assist with model selection.     

农户层面土地利用变化研究综述

论文从研究视角、研究内容、研究方法和手段、数据获取4 个方面对农户层面土地利用变化 研究进行了概括。并归纳出目前研究的几个特点: ①研究较为关注热点地区; ②多将农户层面土地 利用变化的驱动力归因于人口增长所导致的生计压力以及与之相关的农业扩张; ③研究逐步从单 纯关注用途变更转向对用途变更和土地利用集约度变化并重; ④在手段和方法上趋于多样化并趋 向于综合使用多种方法; ⑤土地利用主体决策行为对于土地利用变化影响得到重视; ⑥在分析数据 的获取上, 问卷调查成为主导方式, 但越来越多的研究开始尝试将问卷调查和“3S”技术相结合。最 后总结了已有研究存在的问题, 并探讨了今后农户层面土地利用变化研究发展的方向。     

农牧交错区农户生态足迹及其影响因素

利用农牧交错区典型区域农户调查数据,从生产和消费两个方面分析了农户生态足迹及其影响因素。研究结果表明：1）农户生产足迹远远高于消费足迹,草地生产足迹和草地消费足迹分别对农户总生产足迹和总消费足迹具有决定作用;2）农户生态足迹,尤其是草地生产足迹和草地消费足迹与收入水平具有显著正相关关系,农户从事非农就业一定程度上降低了对当地自然资源的占用和消耗;3）少数民族户、畜牧业收入占比大、人均牧草地多的农户,虽然耕地生产足迹和耕地消费足迹较小,但总生产足迹和总消费足迹、草地生产足迹和草地消费足迹均较大。基于农牧交错区脆弱生态系统,建议通过完善生态补偿机制、改变农户粗放化的畜牧业生产方式、促进农户非农就业,降低农户对本区域生态系统的压力。     

国外生态-经济系统整合模型研究进展

生态-经济系统整合模型的研究将生态的、经济的、社会的以及生物物理模型整合在可持续发展框架中,考察生态系统与经济系统之间的相互作用和反馈关系,采用遥感与计算机建模技术,结合多种生态模型和计量经济模型,预测、模拟、优化生态-经济系统。它的研究范围从整个系统的管理和全球生命支撑系统的可持续性到自然资源和环境服务的可持续使用等等,为分析可持续发展而按照不同的论题建立不同的模型框架。生态经济整合模型的建立是今后生态经济学研究的一个最重要的侧面,生态经济学要想取得主流经济学的地位,必须应用整合的方法深入探索生态经济系统的复杂性和不确定性,真正解决实际问题,为生态与经济的正确决策提供依据。     

Integrated regional modelling and scenario development to evaluate future water demand under global change conditions

Within climate change impact research, the consideration of socioeconomic processes remains a challenge. Socioeconomic systems must be equipped to react and adapt to global change. However, any reasonable development or assessment of sustainable adaptation strategies requires a comprehensive consideration of human-environment interactions. This requirement can be met through multi-agent simulation, as demonstrated in the interdisciplinary project GLOWA-Danube (GLObal change of the WAter Cycle; ). GLOWA-Danube has developed an integrated decision support tool for water and land use management in the Upper Danube catchment (parts of Germany and Austria, 77,000 km²). The scientific disciplines invoked in the project have implemented sixteen natural and social science models, which are embedded in the simulation framework DANUBIA. Within DANUBIA, a multi-agent simulation approach is used to represent relevant socioeconomic processes. The structure and results of three of these multi-agent models, WaterSupply, Household and Tourism, are presented in this paper. A main focus of the paper is on the development of global change scenarios (climate and society) and their application to the presented models. The results of different simulation runs demonstrate the potential of multi-agent models to represent feedbacks between different water users and the environment. Moreover, the interactive usage of the framework allows to define and vary scenario assumptions so as to assess the impact of potential interventions. It is shown that integrated modelling and scenario design not only provide valuable information, but also offer a platform for discussing complex human-environment-interactions with stakeholders.     

畜禽养殖污水生态处理及资源化利用方式的探讨

论述了畜禽养殖场污水的排放特征,并提出污水治理对策。同时阐述了不同类型人工湿地系统用于污水生态处理的优势,并对人工湿地应用畜禽养殖污水资源化的可行性进行分析介绍。     

石化罐区挥发性有机物源强反演技术的研究

石化企业是国民经济的支柱,亦是重大的污染排放源,其中大气污染以VOCs为主,而石化罐区是石化企业最常用的生产装置,研究石化罐区VOCs的核算具有重要的意义。同时,遥感傅里叶变换红外光谱技术以独特的优势,被广泛地应用于大气环境监测中,国内外一些研究学者试图将遥感傅里叶红外光谱技术与VOCs源强反演技术相结合,开发一条VOCs源强核算的新路。基于我国石化罐区的实际情况,提出了一种以遥感傅里叶变换红外光谱技术为监测手段,以石化罐区VOCs源强反演技术-扩散模式反推法为理论基础的石化罐区无组织排放VOCs源强反演的新方法,并介绍了其相关的研究理论、研究方法、研究进展,以及目前所存在的问题,为VOCs源强反演提供了一种新思路。     

海上风电场升压站电气设备可靠性建模

海上升压站是风电场电能传输的关键环节。针对海上升压站电气设备的可靠性研究,提出了一种新颖的基于马尔可夫过程的建模方法。首先对升压站电气设备的运行状态进行划分,确定其二状态马尔可夫模型,其次分析基本元件的连接形式,建立其状态转移概率矩阵和转移概率密度矩阵。在此基础上根据马尔可夫方程建立海上风电场升压站内主要设备变压器、母线,电缆等的可靠性模型,并分析了升压站系统的可靠性指标,最后根据某海上风电场的具体数据,结合所建立的升压站电气设备的可靠性模型,对此风电场的升压站系统的可靠性指标进行计算分析。     

Economic impacts of climatic variability and subsidies on European agriculture and observed adaptation strategies

In order to assess agricultural adaptation to climate impacts, new methodologies are needed. The translog distance function allows assessing interactions between different factors, and hence the influence of management on climate impacts. The Farm Accountancy Data Network provides extensive data on farm characteristics of farms throughout the EU15 (i.e. the 15 member states of the European Union before the extension in 2004). These data on farm inputs and outputs from 1990−2003 are coupled with climate data. As climate change is not the only change affecting European agriculture, we also include effects of subsidies and other changes on inputs and outputs of farms throughout Europe. We distinguish several regions and empirically assess (1) climate impacts on farm inputs and outputs in different regions and (2) interactions between inputs and other factors that contribute to the adaptation to these impacts. Changes in production can partly be related to climatic variability and change, but also subsidies and other developments (e.g. technology, markets) are important. Results show that impacts differ per region, and that ‘actual impacts’ cannot be explicitly separated into ‘potential impacts’ and ‘adaptive capacity’ as often proposed for vulnerability assessment. Farmers adapt their practices to prevailing conditions and continuously adapt to changing conditions. Therefore, ‘potential impacts’ will not be observed in practice, leaving it as a mainly theoretical concept. Factors that contribute to the adaptation also differ per region. In some regions more fertilizers or more irrigation can mitigate impacts, while in other regions this amplifies impacts. To project impacts of future climate change on agriculture, current farm management strategies and their influence on current production should be considered. This clearly asks for improved integration of biophysical and economic models.     

Predicting ectotherm disease vector spread—benefits from multidisciplinary approaches and directions forward

The occurrence of ectotherm disease vectors outside of their previous distribution area and the emergence of vector-borne diseases can be increasingly observed at a global scale and are accompanied by a growing number of studies which investigate the vast range of determining factors and their causal links. Consequently, a broad span of scientific disciplines is involved in tackling these complex phenomena. First, we evaluate the citation behaviour of relevant scientific literature in order to clarify the question “do scientists consider results of other disciplines to extend their expertise?” We then highlight emerging tools and concepts useful for risk assessment. Correlative models (regression-based, machine-learning and profile techniques), mechanistic models (basic reproduction number R ₀) and methods of spatial regression, interaction and interpolation are described. We discuss further steps towards multidisciplinary approaches regarding new tools and emerging concepts to combine existing approaches such as Bayesian geostatistical modelling, mechanistic models which avoid the need for parameter fitting, joined correlative and mechanistic models, multi-criteria decision analysis and geographic profiling. We take the quality of both occurrence data for vector, host and disease cases, and data of the predictor variables into consideration as both determine the accuracy of risk area identification. Finally, we underline the importance of multidisciplinary research approaches. Even if the establishment of communication networks between scientific disciplines and the share of specific methods is time consuming, it promises new insights for the surveillance and control of vector-borne diseases worldwide.     

Farm-level models of spatial patterns of land use and land cover dynamics in the Ecuadorian Amazon

Longitudinal studies examining socio-demographic and other contextual factors are vital to understanding landscape change. Landscape structure, function, and change are assessed for the northern Ecuadorian Amazon by examining the composition and spatial organization of deforestation, agricultural extensification, and secondary plant succession at the farm level in 1990 and 1999 through the integration of data from a satellite time-series, a longitudinal household survey, and GIS coverages. Pattern metrics were calculated at the farm level through the generation of a hybrid land use and land cover (LULC) digital classification of Landsat Thematic Mapper (TM) data. Population, labor, and other household variables were generated from a scientific sample of survey farms or fincas interviewed in 1990 and resurveyed in 1999. Topography, soils, and distance and geographic accessibility measures were derived for sample farms through a GIS as well as qualitative assessments from household surveys. Generalized linear mixed models (GLMMs) were generated for 155 and 157 fincas in 1990 and 1999, respectively, using pattern metrics at the landscape level as dependent variables, and biophysical, geographical, and socio-economic/demographic variables as independent variables. The models were derived to explore the changing nature of LULC at the finca level by assessing the variation in the spatial structure or organization of farm landscapes in 1990 and 1999, and the extent to which this variation could be explained by the available data. Results indicate rapid population growth causing substantial subdivision of plots, which in turn has created a more complex and fragmented landscape in 1999 than in 1990. Key factors predicting landscape complexity are population size and composition, plot fragmentation through subdivision, expansion of the road and electrical networks, age of the plot (1990 only), and topography. The research demonstrates that the process of combining data from household surveys, satellite time-series images, and GIS coverages provide an ideal framework to examine population–environment interactions and that the statistical models presented are powerful tools to combine such data in an integrated way.     

Stochastic trajectory models for turbulent diffusion: Monte Carlo process versus Markov chains

Turbulent diffusion of passive scalars and particles is often simulated with either a Monte Carlo process or a Markov chain. Knowledge of the velocity correlation generated by either of these stochastic trajectory models is essential to their application. The velocity correlation for Monte Carlo process and Markov chain was studied analytically and numerically. A general relationship was developed between the Lagrangian velocity correlation and the probability density function for the time steps in a Monte Carlo process. The velocity correlation was found to be independent of the fluid velocity probability density function, but to be related to the time-step probability density function. For a Monte Carlo process with a constant time step, the velocity correlation is a triangle function; and the integral time scale is equal to one-half of the time-step length. When the time step was chosen randomly with an exponential pdf distribution, the resulting velocity correlation was an exponential function. Other time-step probability density functions, such as a uniform distribution and a half-Gaussian distribution, were also tested.A Markov chain, which presumes one-step memory, has a piecewise linear velocity correlation function with a finite time step. For a Markov chain with a short time step, only an exponential velocity correlation function can be realized. Thus, a Monte Carlo process with random time steps is more versatile than a Markov chain. Direct numerical calculation of the velocity correlation verified the analytical results.A new model which combines the ideas of the Monte Carlo process and the Markov chain was developed. By examining the long-time mean square dispersion, we found an exact solution for the Lagrangian integral time scale of the new model in terms of the intercorrelation parameter and the mean and the variance of the time steps. Using this new model, we can generate any velocity correlation, including one with a negative tail. Two approximate solutions that give upper and lower bounds for the Lagrangian velocity correlation are proposed.     

区域农地市场发育对农户水土保持行为的影响及其空间差异——基于生态脆弱区江西省兴国县、上饶县、余江县村域农户调查的分析

分析农户水土保持决策过程、建立农户水土保持决策的两阶段概念模型,从理论上分析了土地市场对农户水土保持的影响.运用在江西省兴国县、上饶县和余江县的样本村农户调查资料,通过建立土地流转与农户水土保持相互关系模型,运用Heckman的两阶段法对模型参数进行了估计.运用模型估计结果,分析土地流转对农户水土保持决策的影响并且对其区域差异进行了探讨.结果表明:从全部样本来看,农户是否意识到水土流失问题的存在以及家庭土地税费负担量对农户是否采取水土保持决策有显著影响;在兴国县,土地流入面积和费用2个因素对水土保持决策有显著影响;在余江县土地流入面积和费用也有影响显著,并且土地流入面积对农户水土保持投入的影响更强,而且土地流出面积在余江也有显著影响;上饶县,土地流出面积有显著影响.在全部样本、兴国县以及余江县,土地流出收益对农户水土保持决策的影响都不显著.最后,就进一步推进区域水土流失治理促进土地持续利用提出了相关建议.