Environmental Triggers of Winter Annual Weed Emergence in the Midwestern United States

Abstract

Winter annual weeds are becoming prolific in agricultural fields in the midwestern United States. The objectives of this research were to understand the roles of soil temperature (daily average and fluctuation) and moisture on the emergence of nine winter annual weed species and dandelion and to develop predictive models for weed emergence based on the accumulation of modified thermal/hydrothermal time (mHTT). Experiments were established at Lincoln, NE; Mead, NE; and at two sites (irrigated and rainfed) near Clay Center, NE, in 2010 and 2011. In July of each year, 1,000 seeds of each species were planted in 15 by 20 by 6-cm mesh baskets installed between soybean rows. Soil temperature and water content were recorded at the 2-cm depth. Emerged seedlings were counted and removed from the baskets on a weekly basis until no additional emergence was observed in the fall, resumed in late winter, and continued until emergence ceased in late spring. Weather data were used to accumulatemHTT beginning on August 1. A Weibull function was selected to fit cumulative emergence (%) on cumulativemHTT (seven base temperature [Tbase] by six base water potential [Ψbase] by three base temperature fluctuation [Fbase] candidate threshold values = 126 models); it was also fit to days after August 1 (DAA1), for a total of 127 candidate models per species. The search for optimal base thresholds was based on the theoretic-model comparison approach (Akaike information criterion [AIC]). All three components (Tbase, Ψbase, andFbase) were only important for Virginia pepperweed. For downy brome and purslane speedwell, includingTbaseand Ψbaseresulted in the best fit, whereas for dandelion includingTbaseandFbaseresulted in the best fit. A model including onlyTbaseresulted in the best fit for most species included in this study (Carolina foxtail, shepherd's-purse, pinnate tansymustard, henbit, and field pansy). For field pennycress, the model based on DAA1 resulted in the best fit. Threshold values were species specific. Soil temperature was the major environmental factor influencing winter annual weed emergence. Even though soil moisture and often temperature fluctuation are essential for seed germination, ΨbaseandFbasewere not as critical in the predictive models as initially expected. Most seedlings (> 90%) of downy brome, pinnate tansymustard, Carolina foxtail, henbit, and field pansy emerged during the fall. Virginia pepperweed, purslane speedwell, dandelion, shepherd's-purse, and field pennycress seedlings emerged during both fall and spring. The results of this research provide robust information on the prediction of the time of winter annual weed emergence, which can help growers make better management decisions.

Integrating a complex rotation with no-till improves weed management in organic farming. A review

“…Yet, several plants of the spring cohort did not vernalize and produce any seeds. Andersson et al (2002) showed that downy brome emergence was delayed in no-till; in our study, spring emergence of downy brome began in May, 2 to 3 weeks later than it occurs in tilled systems (Werle et al 2014). Finnerty and Klingman (1962) found that downy brome vernalization was greater with short day lengths.…”

Interference Periods in Soybean Crop as Affected by Emergence Times of Weeds

“…(turnip), Bidens pilosa (beggarticks), Ipomoea grandifolia (bindweed), Sida rhombifolia (arrowleaf sida), Richardia brasiliensis (tropical Mexican clover) and Amaranthus viridis (large fruit amaranth) for the three sowing seasons. Weed populations in agricultural crops usually consist of a hierarchy of individuals with one or two dominant species and others of less importance (Werle et al, 2014a).…”

Modelagem matemática do fluxo de emergência de plantas daninhas: ferramenta para decisão no manejo de cultivos

“…Os dados de emergência são convertidos de contagens semanais para emergência cumulativa, com base na emergência total de mudas (Masin et al, 2012;Werle et al, 2014aWerle et al, , 2014b. Da mesma forma, os dados de temperatura e umidade são utilizados para determinar o TT ou TH, de acordo com equação sugerida por Gummerson (1986):…”

“…O conhecimento dos sistemas biológicos onde estas tecnologias são empregadas torna-se fundamental para a implementação de estratégias de manejo (Werle et al, 2014b) e aprimoramento de tecnologias.…”