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Block Reference: #bba80620-d4cc-11eb-b324-35b21b727482
Date and time: Thu, 24 Jun 2021 09:15:35 GMT
Pre-seed wild oats (plants/m 2 )
Using clean seed and cleaning equipment between fields can prevent new weed problems.
Stimulating weed germination
Rotation = oat – pea – wheat – canola
Non PFP = in-crop herbicide used
Std PFP = no in-crop herbicide used
CC = Chaff Cart all years
Disturbance = heavy harrows in the fall in the canola crop
Intensive = intensive management of weeds after the PFP crop in the pea crop, using chaff cart, disturbance, and trifluralin application prior to the pea crop
After allowing weeds to grow for a short time, these weeds must be destroyed, usually with a tillage operation or application of a herbicide. (See section on Stale Seedbed in Spring Tillage)
Table 2. Total density (+/- SE) of volunteer spring wheat in the field (plants/m 2 ) and in the soil seedbank (SB)(seeds/m 2 ) in canola stubble 2002-2004, in a Pesticide Free Production (PFP) rotation study (BRC).
Several US weed scientists have conducted research to develop weed-corn bioeconomic simulation models to help guide decisions regarding herbicide use ( Lybecker et al., 1991) . Weed seed numbers in soil are used to make decisions regarding the application of soil-applied herbicides. Weed densities after corn emergence are used to make post-emergence herbicide application decisions. Bioeconomic models are seen as a potential tool for integrated weed management, allowing growers to tailor their weed management programs to suit the specific weed species and densities in their fields.
Using a model to maximize strategies for herbicide-resistant blackgrass, Cavan et al. (2000) gave estimates on the effectiveness of various strategy options. Based on research with a long-term model for control of blackgrass and annual bluegrass, Munier-Jolain et al. (2002) concluded that threshold-based weed management strategies can be more cost-effective than spraying every year and may enable important reductions in herbicide use. However, the highest long-term profitability was obtained for the lowest weed level threshold tested.
Müller-Schärer et al. (2000) reviewed the progress made during 1994–1999 by 25 institutions within 16 European countries on biological weed control. These efforts were aimed at control of major weed species, including common lambsquarters, common groundsel, and species of pigweed, broomrape and bindweed in major crops, including corn and sugar beet. No practical control has yet been reached for any of the five target weeds, however, the authors concluded that potential solutions have been identified.
Preventive Weed Management in Direct-Seeded Rice
Weed seedbank replenishment can also be avoided by preventing production and shedding of new seeds. This can be obtained as an outcome of increased competition or as an effect of a well planned crop rotation ( Légère et al., 2011 ). However, it is also important to prevent seed shedding from late emerging weeds that, although usually unable to diminish crop yield in the same growing season, may create potential weed problems in subsequent crops or growing seasons through their seed inputs. Similarly, it is important to avoid weed seed shedding (e.g., by stubble cultivation or mowing) in the period between two crop growing cycles, an important issue that many farmers tend to disregard.
The use of atrazine in corn has helped to reduce significantly the number of weed seeds in the soil. In a Colorado experiment, a field that began with 1.3 billion buried weed seeds per acre (2.7 billion/ha) was treated with atrazine for 6 years. Afterwards, the weed seed population had been reduced to 20 million/A (50 million/ha). In this experiment, atrazine was discontinued on half the plot after the third year, when 407 million buried weed seeds were counted per acre (1006 million/ha). After three additional years of no atrazine use, the weed seed population had increased to 648 million/A (1600 million/ha) ( Schweizer and Zimdahl, 1984 ).
As discussed in Section 5.2.4 , the core of weed management rests on minimizing the weed seed rain and therefore minimizing the weed seedbank. This is why managing the weed seed rain is the most important part of integrated organic weed management and must therefore be the first priority.
Two bioeconomic weed control models have been developed through cooperative work between USDA's Agricultural Research Service (ARS) and university experts: WEEDCAM (Colorado) and WEEDSIM (Minnesota). WEEDSIM was field tested for 1991–1994. After 4 years of applying WEEDSIM recommendations to the same plots, there were no increases in annual weed densities or decreases in weed control or crop yields as compared to standard herbicide management systems for the region ( Forcella et al., 1996) . In most cases, the model-generated treatments controlled weeds as well as the standard herbicide treatment. The quantity of herbicide active ingredient applied decreased 27% with the seedbank model and 68% with the seedling model, relative to the standard herbicide treatment ( Buhler et al., 1996) .