Deterministic crop growth models require several inputs relating to crop/variety, soil physical properties, weather and crop management. The input values used could be significantly uncertain due to random and systematic measurement errors and spatial and temporal variation observed in many of these inputs. Often soil and weather data are approximated using GIS and/or weather generators. In this paper total uncertainty in simulated yield, evapotranspiration and crop N uptake has been quantified considering uncertainties in crop, soil and weather inputs. WTGROWS, a crop model that simulates the effect of genotypic, climatic, edaphic and management factors on productivity of spring wheat was used. The uncertainty in each input was represented by a statistical distribution of values based on literature review, actual measurement and subjective expert judgement. The Monte Carlo simulation technique was used to analyze total uncertainty. The results showed that uncertainties in crop, soil and weather inputs resulted in uncertainty in simulated grain yield, ET and N uptake, which varied depending upon the production environment. Uncertainties in outputs increased as the production system changed from a potential production level to a level where crop growth was constrained by limited availability of water rand nitrogen. There was an 80% probability that the bias in the deterministic model outputs was always less than 10% in potential and irrigated production systems. In rainfed environments this bias was larger. The bias in simulated outputs was less than or equal to model error. Most of the uncertainty in outputs caused by variable soil, crop and weather inputs could be represented if the outputs were determined using fixed soil and crop data, and a large series of weather data. In potential and irrigated production systems, inputs relating to crop photosynthesis and leaf area estimation had a large 'uncertainty importance'. Uncertainties in soil N inputs and vapor pressure were also of great importance in irrigated environments. In rainfed environments, uncertainties in soil and weather inputs were dominant and crop parameters had only limited 'uncertainty importance'. The implications of these results in estimates of potential and rainfed productivity, database development and guiding refinement of models are discussed.
