The impact of potential errors in rainfall observation on the simulation of crop growth, development and yield

摘要: Abstract Precipitation is the source for almost all soil moisture available plant extraction by crops that are grown in rainfed cropping systems. In many cases precipitation measured using a tipping-bucket rain gauge (TBRG) or similar device. Most automated weather station (AWS) networks employ TBRG to observe rainfall, due need of automating process collecting rainfall data. However, it known these gages have some type error aerodynamic effects, wetting losses and actual design operation sensor. The data collected AWS frequently used as input complex computer simulation models predict crop yield function conditions management scenarios. objective this study was evaluate impact potential errors observations on simulated growth, development yield. generic grain legume model cropgro cereal ceres were simulate growth soybean, peanut, wheat maize under different These use daily inputs. study, 36 years historical records from Tifton, Georgia obtained randomly modified with relative when values greater than zero emulate inaccuracy observations. Two approaches considered: (a) gauges underestimate (negative bias) (b) overestimate (positive rainfall. To account random variability avoid trend could affect development, modifications replicated 32 times each individual year case considered. amounts did not phenology, but resulted substantial changes both mean yield, biomass, evapotranspiration, drainage. underestimation, e.g. negative bias, measurements had larger variables an overestimation. This showed accuracy critical outputs directly correlated It also demonstrated soil–plant–atmosphere sensitive variation possibly other environmental