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40 2019 ND-CP 413905
- Điều hướng trang này:
- SOLUS methodology
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MicroLEIS (De la Rosa et al., 1992) is an integrated system for land data transfer and agro-ecological land evaluation. This system provides a computer-based set of tools for an orderly arrangement and practical interpretation of land resources and agricultural management data. Its major components are: ¾land evaluation using the following spatial units: place (climate), soil (site+soil), land (climate+site+soil) and field (climate+site+soil+management); ¾data and knowledge engineering through the use of a variety of georeferenced database, computer programs, and boolean, statistical, expert system and neural network modelling techniques; ¾monthly meteorological data and standard information as recorded in routine land surveys; ¾integrated agro-ecological approach, combining biophysical data with agricultural management experience; and ¾generation of data output in a format readily accepted by GIS packages. Recently two components have been added in order to comply with rising environmental concerns (De la Rosa et al., 2001): prediction of global change impacts by creating hypothetical scenarios; and incorporating the land use sustainability concept through a set of tools to calculate current status; potentiality and risks; impacts; and responses. Based on the concepts developed in LEFSA and tools from farming systems analysis, the SOLUS methodology (Sustainable options for land use) was developed for land use analysis at field to regional scales (Bouman et al., 1998). The methodology consists of technical coefficient generators to quantify inputs and outputs of production systems, a linear programming model that selects production systems by optimizing regional economic surplus, and a geographic information system. The so-called technical coefficient generators include LUCTOR, a combination of a crop model and an expert model to define crop options according to crop type and management practice, and PASTOR, a pasture and animal expert system (Hengsdijk et al., 1999). The linear programming model selects land use scenarios by optimizing or maximizing a specific objective function under a set of coherent restrictions (Schipper et al., 2000). Economic sustainability indicators include economic surplus and employment, while biophysical sustainability is translated into soil N-P-K balances, biocide use, greenhouse gas emissions, and nitrogen loss. Exchanges between economic and sustainable objectives are quantified for different scenarios and alternative land use systems generated by the technical coefficient generators (Bouman et al., 1999). Land use scenarios can be implemented by changing economic conditions, imposing sustainability restrictions in the optimization process, and incorporating alternative production systems based on different technologies in the technical coefficient generators. GIS is used for storage, spatial manipulation and visualization of input and output data. ISLE, Intelligent System for Land Evaluation, automates the process of land evaluation and graphically illustrates the results on digital maps (Tsoumakas and Vlahavas 1999). Its main features are the support of GIS capabilities on the digital map of an area, and the support of expert analysis of regions of this area, through a single sophisticated user interface. ISLE models the evaluation of land in accordance with the SYS model for land evaluation (Sys et al., 1991a and b, 1993). LUCIE, developed by the Centre for computer-based learning in land use and environmental sciences in Aberdeen, stands for Land-use capability investigation and evaluation. This computer-assisted learning package allows students to explore a complex landscape in the safety of a laboratory, to evaluate land units and to produce maps of land capability. CYSLAMB, Crop Yield Simulation and Land Assessment Model for Botswana (Tersteeg 1994), is a dynamic biomass model that relies on the input of historical climatic data to model potential crop production. In this way, scenarios are based on actual data Land evaluation – towards a revised framework 12 compiled in different rainfall periods. Other inputs include detailed information about soil conditions and crop management systems. A statistical analysis identifies different potential yield levels that could be achieved by different crop production systems. The 75 percent quartile yield represents potential yield levels that would be exceeded in three-fourths of all years. This yield level therefore can be considered as a dependable yield. The model has been validated for the five main crops in Botswana (maize, millet, sorghum, groundnuts and cowpeas). The model combines physical and socio-economic parameters in the calculation of potential yield levels. In addition to information about physical parameters or land characteristics in FAO terminology, a number of management-related variables reflecting the socio-economic conditions of the farmer are included: date of ploughing, date of planting, number of planting opportunities used, date of weeding and percentage weed cover. The management variables can be adjusted to reflect differences in farmers’ socio- economic conditions, such as the availability of household labour, draught power, tools and fertilizer, income levels, non-agricultural incomes, livestock-crop interactions, etc. This facility makes CYSLAMB a flexible tool that can model crop production based on physical and socio-economic conditions at several levels, ranging from village to district and national scale. The results can serve as input to gross-margin calculations to compare the performance of a range of alternative production systems and thereby assist decision-makers in their choice between different land use options. tải về 0.57 Mb. Chia sẻ với bạn bè của bạn:
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