Burial and subsequent growth of rigid ryegrass (Lolium rigidum) and ripgut brome (Bromus diandrus) following strategic deep tillage

Author:

Borger Catherine P. D.ORCID,Mwenda GeorgeORCID,Collins Sarah J.ORCID,Davies Stephen L.ORCID,Peerzada Arslan MasoodORCID,van Burgel AndrewORCID

Abstract

AbstractSoil amelioration via strategic deep tillage is occasionally utilized within conservation tillage systems to alleviate soil constraints, but its impact on weed seed burial and subsequent growth within the agronomic system is poorly understood. This study assessed the effects of different strategic deep-tillage practices, including soil loosening (deep ripping), soil mixing (rotary spading), or soil inversion (moldboard plow), on weed seed burial and subsequent weed growth, compared with a no-till control. The tillage practices were applied in 2019 at Yerecoin and Darkan, WA, and data on weed seed burial and growth were collected during the following 3-yr winter crop rotation (2019 to 2021). Soil inversion buried 89% of rigid ryegrass (Lolium rigidum Gaudin) and ripgut brome (Bromus diandrus Roth) seeds to a depth of 10 to 20 cm at both sites, while soil loosening and mixing left between 31% and 91% of the seeds in the top 0 to 10 cm of soil, with broad variation between sites. Few seeds were buried beyond 20 cm despite tillage working depths exceeding 30 cm at both sites. Soil inversion reduced the density of L. rigidum to <1 plant m−2 for 3 yr after strategic tillage. Bromus diandrus density was initially reduced to 0 to 1 plant m−2 by soil inversion, but increased to 4 plants m−2 at Yerecoin in 2020 and 147 plants at Darkan in 2021. Soil loosening or mixing did not consistently decrease weed density. The field data were used to parameterize a model that predicted weed density following strategic tillage with greater accuracy for soil inversion than for loosening or mixing. The findings provide important insights into the effects of strategic deep tillage on weed management in conservational agricultural systems and demonstrate the potential of models for optimizing weed management strategies.

Publisher

Cambridge University Press (CUP)

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