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Title:   Author  item
                                            Spokas, Kurtitem
                                            Forcella, Frank
  Submitted to: Weed Science
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 12/5/2008
                                    Publication  Date: 3/1/2009
                                    Citation: Spokas, K.A., Forcella, F. 2009. Software Tools for Weed Seed Germination Modeling. Weed Science. 57(2):216-227.Interpretive Summary: The next generation of weed seed germination models will need to account for variable soil microclimate conditions. In order to predict this microclimate environment we have developed a suite of individual tools (models) that can be used in conjunction with the next generation of weed seed germination models. The three tools that are outlined in this manuscript are Global TempSIM, Global RainSIM, and the Soil Temperature and Moisture Model (STM^2).  Overall, the climate prediction models compared favorably to observations for the validation locations that were selected from around the globe. Both the GlobalTempSIM and GlobalRainSIM duplicated the yearly pattern of air temperatures and precipitation at each location. STM^2 also performed well in comparisons to actual soil temperatures as well as predicting the pattern of annual soil moisture. Improved model fits occurred when weather data were available from the same location as the soil temperature and moisture measurements. These models utilize minimum climatic inputs (maximum air temperature, minimum air temperature, and precipitation) as well as the latitude/longitude of the field site (embedded tools allow the entry of US zip code to find latitude/longitude). e models are extremely user friendly, with embedded empirical relationships to lower the input requirements of typical soil physics models. All of these tools are freely available for download from the USDA-ARS website (www.ars.usda.gov). We foresee that these models will be useful for farmers, researchers, and others requiring knowledge or planning tools for environmental conditions and simulating soil conditions at global locations.
                                Technical Abstract: The next generation of weed seed germination models will need to account for variable soil microclimate conditions. In order to predict this microclimate environment we have developed a suite of individual tools (models) that can be used in conjunction with the next generation of weed seed germination models. The three tools that will be outlined here are Global TempSIM, Global RainSIM, and the Soil Temperature and Moisture Model (STM^2). Each model was compared to several sets of observed data from worldwide locations. Overall, the climate predictors compared favorably.  GlobalTempSIM had a bias between -2.7 and +0.9 degree C, mean absolute errors between 1.9 and 5.0 degree C, and an overall d-index of 0.79 to 0.95 for the 11 global validation sites in 2007.  GlobalRainSIM duplicated the yearly pattern of precipitation at each location, and correspondingly had a bias for cumulative precipitation ranging from -210 to +305 mm, a mean absolute error between 29 and 311 mm, and a corresponding d-index of 0.78 to 0.99 for the sites and years compared. The high d-indices indicate that the models adequately captured the annual patterns in both temperature and precipitation for the validation sites. STM^2 also performed well in comparisons to actual soil temperatures with a range of -2 to +1.5 degree C biases and mean absolute errors between 1.4 and 4.4 degree C for the annual cycle at the validation sites. The overall d-index ranged from 0.89 to 0.97 for the soil temperature comparisons. The soil moisture prediction annual bias was between +0.05 and +0.21 cm3 cm-3, mean absolute errors ranging from 0.08 to 0.20 cm3 cm-3, and possessed d-indices between 0.43 and 0.94 for the validation sites. There were two sites where soil moisture potential measurements were compared for various years leading to an annual bias of -78 to -12 kPa, mean absolute errors between 41 and 101 kPa, and d-indices between 0.70 and 0.92 for the annual cycles compared. Improved model fits occurred when weather data were available from the same location as the soil microclimate measurements. All of these tools are freely available for download from the USDA-ARS website (www.ars.usda.gov). These models were developed in JAVA, are simple to use, and they operate on multiple platforms (e.g. Mac, PC, Sun).


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