农业生态系统模式的优化设计探讨

对农业生态系统模式的优化设计原则、内容、方法与步骤进行了探讨。认为模式优化设计包括5大原则，即区域适宜性原则、实用性原则、经济与市场原则、持续性原则和科学性原则；农业生态系统模式设计的主要内容包括总体目标设计、产业结构设计、时空结构设计、食物链网设计、环境与生态形象设计、物质与资金投放和输入输出设计、技术体系设计、面向对象设计及系统集成与优化；农业生态系统设计的基本程序包括系统环境辨识、系统诊断、系统模型分析和方案设计、系统评价和方案选优、系统运行及反馈修正等内容。     

A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling

During the last 50 years, the management of agroecosystems has been undergoing major changes to meet the growing demand for food, timber, fibre and fuel. As a result of this intensified use, the ecological status of many agroecosystems has been severely deteriorated. Modeling the behavior of agroecosystems is, therefore, of great help since it allows the definition of management strategies that maximize (crop) production while minimizing the environmental impacts. Remote sensing can support such modeling by offering information on the spatial and temporal variation of important canopy state variables which would be very difficult to obtain otherwise.In this paper, we present an overview of different methods that can be used to derive biophysical and biochemical canopy state variables from optical remote sensing data in the VNIR-SWIR regions. The overview is based on an extensive literature review where both statistical–empirical and physically based methods are discussed. Subsequently, the prevailing techniques of assimilating remote sensing data into agroecosystem models are outlined. The increasing complexity of data assimilation methods and of models describing agroecosystem functioning has significantly increased computational demands. For this reason, we include a short section on the potential of parallel processing to deal with the complex and computationally intensive algorithms described in the preceding sections.The studied literature reveals that many valuable techniques have been developed both for the retrieval of canopy state variables from reflective remote sensing data as for assimilating the retrieved variables in agroecosystem models. However, for agroecosystem modeling and remote sensing data assimilation to be commonly employed on a global operational basis, emphasis will have to be put on bridging the mismatch between data availability and accuracy on one hand, and model and user requirements on the other. This could be achieved by integrating imagery with different spatial, temporal, spectral, and angular resolutions, and the fusion of optical data with data of different origin, such as LIDAR and radar/microwave.     

Brown and green LAI mapping through spectral indices

When crops senescence, leaves remain until they fall off or are harvested. Hence, leaf area index (LAI) stays high even when chlorophyll content degrades to zero. Current LAI approaches from remote sensing techniques are not optimized for estimating LAI of senescent vegetation. In this paper a two-step approach has been proposed to realize simultaneous LAI mapping over green and senescent croplands. The first step separates green from brown LAI by means of a newly proposed index, ‘Green Brown Vegetation Index (GBVI)’. This index exploits two shortwave infrared (SWIR) spectral bands centred at 2100 and 2000 nm, which fall right in the dry matter absorption regions, thereby providing positive values for senescent vegetation and negative for green vegetation. The second step involves applying linear regression functions based on optimized vegetation indices to estimate green and brown LAI estimation respectively. While the green LAI index uses a band in the red and a band in the red-edge, the brown LAI index uses bands located in the same spectral region as GBVI, i.e. an absorption band located in the region of maximum absorption of cellulose and lignin at 2154 nm, and a reference band at 1635 nm where the absorption of both water and dry matter is low. The two-step approach was applied to a HyMap image acquired over an agroecosystem at the agricultural site Barrax, Spain.     

Farmer’s adaptive strategies on land competition between societal outcomes and agroecosystem conservation in the purple-soiled hilly region, southwestern China

The worldwide extension and intensification of farming during the last century has led to ecosystem degradation and caused a series of environmental problems.Conservation of ecosystem services in agricultural regions has been implemented by top-down government actions or initiated by resilience scientists in the developed countries,but little attention was paid in the developing countries,especially in some remote mountainous regions.The present paper presents a case study showing how local farmers obtained both maximal societal outcomes and agroecosystem conservation interests in the absence of distinct boundaries between agricultural and protected ecological areas in the densely populated purple-soiled hilly region of southwestern China.The local community(Yanting County) has developed a mosaic agricultural-forestry-fishery-stock breeding system with spatially targeted land uses,diverse agricultural productions and multiple ecological partnerships.It indicates that the local farmers have hereditarily perceived sound strategies on maximizing sustainable societal outcomes and optimizing tradeoffs among macro-market,state policy,new technological facility and ecological reinforcement.