Publications by Colleges and Departments (MSU - Bozeman)
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/3
Browse
2 results
Search Results
Item Impact of species identity and phylogenetic relatedness on biologically-mediated plant-soil feedbacks in a low and a high intensity agroecosystem(2014-12) Miller, Zachariah J.; Menalled, Fabian D.Aims: Plant species-specific effects on soil biota and their impacts on subsequent plant growth, i.e. plant-soil feedbacks (PSFs, henceforth), are major drivers in natural systems but little is known about their role in agroecosystems. We investigated the presence and magnitude of PSFs in two contrasting agricultural settings and tested the importance of species identity and phylogenetic relationships in determining PSFs. Methods: We compared PSFs that developed from an intensified agricultural site and a nearby non-cultivated pasture. Four weed and seven crop species were grown in soil inoculated with either biologically active or sterilized soils from each system. Four crop response species were grown to estimate PSFs. Results: PSFs were species-specific. The identity of currently- and previously-planted species and their interactions explained over 80 % of the variation in feedbacks. Biota from the intensified agricultural site produced negative feedbacks in three of the four response species. Phylogenetic relationships partially explained PSFs. Conclusions: PSFs can alter crop growth and may be altered by agricultural practices. The species-specific effect to soil biota should be taken into account when assessing the extent to which crop and weed species could influence subsequent plant growth.Item Interacting agricultural pest management practices and their effect on crop yield: Application of a Bayesian decision theory approach to the joint management of Bromus tectorum and Cephus cinctus(2015-02) Keren, Ilai N.; Menalled, Fabian D.; Weaver, David K.; Robinson-Cox, James F.Worldwide, the landscape homogeneity of extensive monocultures that characterizes conventional agriculture has resulted in the development of specialized and interacting multitrophic pest complexes. While integrated pest management emphasizes the need to consider the ecological context where multiple species coexist, management recommendations are often based on single-species tactics. This approach may not provide satisfactory solutions when confronted with the complex interactions occurring between organisms at the same or different trophic levels. Replacement of the single-species management model with more sophisticated, multi-species programs requires an understanding of the direct and indirect interactions occurring between the crop and all categories of pests. We evaluated a modeling framework to make multi-pest management decisions taking into account direct and indirect interactions among species belonging to different trophic levels. We adopted a Bayesian decision theory approach in combination with path analysis to evaluate interactions between Bromus tectorum (downy brome, cheatgrass) and Cephus cinctus (wheat stem sawfly) in wheat (Triticum aestivum) systems. We assessed their joint responses to weed management tactics, seeding rates, and cultivar tolerance to insect stem boring or competition. Our results indicated that C. cinctus oviposition behavior varied as a function of B. tectorum pressure. Crop responses were more readily explained by the joint effects of management tactics on both categories of pests and their interactions than just by the direct impact of any particular management scheme on yield. In accordance, a C. cinctus tolerant variety should be planted at a low seeding rate under high insect pressure. However as B. tectorum levels increase, the C. cinctus tolerant variety should be replaced by a competitive and drought tolerant cultivar at high seeding rates despite C. cinctus infestation. This study exemplifies the necessity of accounting for direct and indirect biological interactions occurring within agroecosystems and propagating this information from the statistical analysis stage to the management stage.