Módszerek összehasonlítása

	Crop Simulation Modeling	Farming Systems Research and Extension
Tudományterület	Food Agriculture Studies	Food Agriculture Studies
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2003	2000
Megalkotó	James W. Jones et al. (DSSAT); Dean Holzworth et al. (APSIM)	Michael Collinson and the international farming-systems research community (CIMMYT/CGIAR)
Típus	Process-based dynamic simulation pipeline for crop growth and yield	Iterative diagnostic and adaptive on-farm research pipeline
Alapmű	Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., & Ritchie, J. T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3-4), 235-265.	Collinson, M. P. (Ed.) (2000). A History of Farming Systems Research. Wallingford, UK: CABI Publishing & FAO. ISBN: 9780851994055
Alternatív nevek	Crop Growth Simulation, Process-Based Crop Modeling, Crop Systems Modeling, Dynamic Crop Modeling	FSR/E, Farming Systems Research, On-Farm Client-Oriented Research, Whole-Farm Systems Research
Kapcsolódó	4	4
Összefoglaló	Crop simulation modeling uses process-based, dynamic computer models to predict how a crop grows and yields under specified weather, soil, and management, by numerically integrating mechanistic equations for development, photosynthesis, and water and nutrient balances on a daily time step. The two most widely used platforms are DSSAT, documented by James Jones and colleagues in 2003, and APSIM, whose modern architecture was described by Dean Holzworth and colleagues in 2014. Rather than fitting a statistical curve to yield data, these models encode the underlying biophysics — temperature-driven phenology, radiation-use efficiency, soil water and nitrogen dynamics — so they can extrapolate to weather, soils, and management combinations never directly observed. This makes crop models powerful tools for in silico experimentation, scenario analysis, and climate-change and management impact assessment where field trials alone would be impossibly slow or costly.	Farming Systems Research and Extension (FSR/E) is an iterative, client-oriented research methodology that treats the smallholder farm as a whole interacting system rather than a collection of isolated crops, and designs technology around the actual circumstances and goals of homogeneous groups of farmers. Developed within CIMMYT and the wider CGIAR system from the 1970s and synthesized in Michael Collinson's 2000 history, FSR/E proceeds by diagnosing the whole farm, grouping farmers into recommendation domains who share circumstances, ranking their binding constraints, and then testing candidate technologies in farmer-managed on-farm trials whose results feed back into the next diagnostic cycle. Its defining commitment is that research priorities and experimental designs should follow from farmers' resources, constraints, and objectives, so that recommendations are not just statistically valid on a research station but adoptable on real fields.