Development of a Damage Function Model for Pratylenchus penetrans on Corn

The lesion nematode Pratylenchus penetrans is a common pest of corn in the north-central United States. There are relatively few studies documenting the impact of Pratylenchus spp. on grain yield even though they are recognized as pests of corn and the target of commercial seed treatments. We adapted a component error modeling approach to develop a damage function for P. penetrans that included the influence of year and site in the yield loss relationship. Field data from six site-years was used to derive panel data consisting of all pairwise comparisons of the difference in nematode population densities and the associated proportional yield difference. Fourteen regression models of the relationship between proportional yield loss and the difference in nematode density were developed from soil and root assays at different corn growth stages. Seven models were significant: four models based on nematode population densities in soil (initial and final samples) and three based on nematode densities in seminal roots (corn growth stages V1 to V2 and V6) and adventitious roots (corn growth stage R1 to R2). The model we consider to be the most important, that based on the initial soil assay, estimated the yield loss caused by each nematode to be 0.0142%. The grand mean of the 118 plots we sampled implied a yield loss of 3.79%. The random effects of year and field did not contribute significantly to any of the models but were close to significance for some, suggesting a benefit from larger data sets. Experimental error was the largest component of the variance for all of the models; therefore, the damage function is more useful for demonstrating impact of P. penetrans rather than for accurately predicting yield loss at the field level. All of the fields in our study were an irrigated loamy sand soil, with grain yields above the county average; therefore, it is possible that our damage function is conservative. The value of soil sampling has been questioned for P. penetrans and this study shows it to be equal to if not better than root assays for predicting yield.

An appraisal of soil phosphorus testing data for crops and pastures in South Australia

Data from more than 580 field experiments conducted in South Australia over the past 30 years have been re-examined to estimate extractable soil phosphorus (P) levels related to 90% maximum yield (C90) for 7 crop species (wheat, barley, oilseed rape, sunflower, field peas, faba beans, potato) and 3 types of legume-based pasture (subterranean clover, strawberry clover, annual medics). Data from both single-year and longer term experiments were evaluated. The C90 value for each species was derived from the relationship between proportional yield responsiveness to applied P fertiliser rates (determined as grain yield in crops and herbage yield in ungrazed pastures) and extractable P concentrations in surface soils sampled before sowing. Most data assessments involved the Colwell soil P test and soils sampled in autumn to 10 cm depth. When all data for a species were considered together, the relationship between proportional yield response to applied P and soil P status was typically variable, particularly where Colwell soil P concentration was around C90. When data could be grouped according to common soil types, soil surface texture, or P sorption indices (selected sites), better relationships were discerned. From such segregated data sets, different C90 estimates were derived for either different soil types or soil properties. We recommend that site descriptors associated with the supply of soil P to plant roots be determined as a matter of course in future P fertiliser experiments in South Australia. Given the above, we also contend that the Colwell soil P test is reasonably robust for estimating P fertiliser requirements for the diverse range of soils in the agricultural regions of the State. In medium- and longer term experiments, changes in Colwell soil P concentration were measured in the absence or presence of newly applied P fertiliser. The rate of change (mg soil P/kg per kg applied P/ha) appeared to vary with soil type (or soil properties) and, perhaps, cropping frequency. Relatively minor changes in soil P status were observed due to different tillage practices. In developing P fertiliser budgets, we conclude that a major knowledge gap exists for estimating the residual effectiveness of P fertiliser applied to diverse soil types under a wide range of South Australian farming systems.