P. 2005. Low dispersal ability and habitat specificity promote extinctions in rare but not in widespread species: the Orthoptera of Germany. Á/ Ecography 28: 593 Á/602.Local extinctions are often non-randomly associated with range size, dispersal ability and habitat specificity, as well as body size, sexual dimorphism and phylogeny. We used a large data set of the Orthoptera species (bush crickets, crickets, grasshoppers) occurring in Germany and compared the number of occupied grid cells before 1980 to those occupied after 1980, corrected for monitoring intensity. The number of grid cells in which a species went extinct was non-linearly related to the number of occupied grid cells per species. Using generalized linear modelling we analysed extinction in relation to national distribution (the number of occupied grid cells before 1980), dispersal ability (derived from a large body of literature concerning wing development, colonization dynamics and within-habitat mobility), habitat specificity (moisture specialists versus generalists), potential reproduction (the number of ovarioles), the degree of sexual size dimorphism and phylogeny (twelve clades). Species with a large global range size also had a large national range size. Species with a large range experienced more total extinction events than species with smaller ranges but relatively fewer compared to range size. The latter relationship was largely shaped by the dispersal ability of the species: the interactions of range size )/dispersal ability and range size )/habitat specificity explained almost one third of the variation in the number of extinction events. Species with high dispersal ability went extinct in a similar number of grid cells irrespective of their range size. By contrast, species with low dispersal ability went extinct in proportion to their range size. Therefore, comparing the speed of extinction across species in the conventional way of extinction rates (that is the percentage of range contraction) might be flawed because it only applies to species with low dispersal ability. Sexual size dimorphism was not a significant predictor of extinction. Extinction was not concentrated on particular clades.

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Parameterization is a technique used by modelers to approximate the response of a physical system using an empirical function. This technique has the advantages that it reduces the complexity of models and often simplifies their input requirements, making the models more easily usable for operational purposes. In this study, a relatively sim· pie model of leaf area and dry mass growth for the gramineous crops was developed to demonstrate the use of parameterization. Results of model simulations for grain sorghum (Sorghum bicolor (L.) Moench), corn (Zea mays L.), and spring wheat (Triticum aestivum L.) were in reasonable agreement with field observations. The effects of plant population, fertilization level, and water stress on crop growth were implicitly accounted for in the simulations through the appropriate assignment of parameter values in empirical functions controlling leaf area appearance and senescence.Stephen J. Maas, USDA-ARS, Subtropical Agric. Res. Lab., 2413 East Highway 83, Weslaco, TX 7859Cr8344.

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Large‐area yield prediction early in the growing season is important in agricultural decision‐making. This study derived maize (Zea mays L.) leaf area index (LAI) estimates from spectral data and used these estimates with a simple LAI‐based yield model to forecast yield under irrigated conditions in large areas in Sinaloa, Mexico. Leaf area index was derived from satellite data with the use of an equation developed with LAI measurements from farmers' fields during the 2001–2002 autumn–winter growing season. These measurements were correlated with the normalized difference vegetation index values from 2002 Landsat ETM+ (enhanced thematic mapper) data. The equation was then tested with 2003 Landsat imagery data. A yield model was validated with maximum LAI and yield data measured in farmers' fields in northern and central Sinaloa during three consecutive autumn–winter growing seasons (1999–2000, 2000–2001, and 2001–2002). The yield model was further validated with 2002–2003 autumn–winter ground LAI (gLAI) and satellite‐derived LAI (sLAI) data from 71 farmers' fields in northern and central Sinaloa. Grain yield was predicted with a mean error of −9.2% with maximum gLAI and −11.2% with sLAI. Results indicate that the yield model using LAI can forecast yield in large areas in Sinaloa in the middle of the growing season with a mean absolute error of −1.2 Mg ha−1. The use of sLAI in place of ground measurements increased the mean absolute error by 0.3 Mg ha−1. Nevertheless, the use of sLAI would eliminate laborious LAI measurements for large‐area yield prediction in Sinaloa.

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The accuracy of parameterized crop growth models is dependent on obtaining the proper parameter values that control the response of the model to environmental conditions. In this study, a method is described for objectively evaluating model parameters based on ob· servations of crop growth obtained during the growing season. An iterative numerical procedure is used to systematically manipulate the values of selected parameters and initial conditions until the model simulation matches the observations of growth. Examples involving a previously-developed growth model indicate that reasonably accurate simulations can be made for three gramineous crops (grain sorghum, com, and spring wheat) by calibrating a set of parameters and initial conditions that affect leaf area appearance and senescence. It is proposed that the effectiveness of this procedure in using infrequent growth observations and the ability to obtain these observations using non· destructive measurement techniques make this modelling procedure feasible for use in practical agricultural applications.Stephan J. Maas, USDA-ARS, Subtropical Agric. Res. Lab., 2413East Highway 83, Weslaco, TX 78596--8344.

It has been proposed that remotely sensed information from satellites could complement the performance of crop growth models. A study was conducted to determine how a model could effectively utilize satellite data and if model estimates of crop yield could be significantly improved by satellite data. A simple model was developed for simulating growth and yield of grain sorghum (Sorghum bicolor (L.) Moench) on an individual-field basis. The model contained three state variables (stage of development, green leaf area index [GLAI), and aboveground dry mass) and utilized daily meteorological observations. When available, GLAI values for target fields could be used to adjust the initial values of the state variables until a "best fit" of the observed data was iteratively achieved by the simulation. GLAI values could be obtained by ground-based measurements, but satellite observations were demonstrated to be an appropriate source of data for initializing the model. The model was developed and verified using data from 10 fields observed in Central Texas in 1976. The model was tested using a completely independent data set containing yield and satellite observations from 37 fields in South Texas in 1973Texas in , 1975Texas in , 1976Texas in , and 1977. Without using the initializing procedure, the average yield for the 37 fields was underestimated by approximately 30%. Use of satellite-derived GLAI data to initialize the same simulations resulted in a 2% overestimate of average yield. The results of this study confirm the usefulness of the initializing procedure and satellite data to improve model estimates of crop yield.