Abstract
Abstract A study assessed the potential for using cumulative growing degree days (CGDD) to predict the weed emergence periodicity of three weed species: Argemone mexicana , Brassica tournefortii , and Rapistrum rugosum . Weed emergence was monitored regularly by placing 200 fresh seeds of each weed species on the soil surface. Weed emergence data was fit using a three-parameter sigmoidal Gompertz model. The CGDD required for 50% emergence of A. mexicana ranged from 3380 to 5302, depending upon the seasonal variation in temperature and rainfall. The majority of emergence appeared from March to June. The seeds of A. mexicana exhibited dormancy, as the majority of seeds germinated in the second season. The CGDD required for 50% emergence of B. tournefortii ranged from 824 to 2311, depending upon the seasonal variation in temperature and intensity of rainfall. Most cohorts of B. tournefortii appeared in the first season from February to June, indicating little dormancy in seeds. The CGDD required for 50% emergence of R. rugosum ranged from 2242 to 2699, depending upon weather parameters (temperature and rainfall). The main cohorts of R. rugosum appeared from February to June, and 60% of seeds germinated in the first season, while 40% germinated in the second season, indicating dormancy in seeds. The coefficients of determination for the model verification on the emergence pattern of three weeds were > 85%, suggesting that CGDD are good predictors for the emergence of these weeds. These results suggest that forecasting the emergence of three weed species on the basis of CGDD and rainfall patterns will help growers to make better weed management decisions.
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Claims 11
Claim · model-written summary
Model verification of the CGDD-based emergence model gave coefficients of determination above 85% for the three weeds.
Verbatim
e0“The coefficients of determination for the model verification on the emergence pattern of three weeds were > 85%”
Claim · model-written summary
Cumulative growing degree days required for 50% emergence of Argemone mexicana ranged from 3380 to 5302 depending on seasonal temperature and rainfall.
- Taxon
- A. mexicana
Argemone mexicanae0 - Outcome
- CGDD required for 50% emergencee0
- Value
- 3380 to 5302e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The CGDD required for 50% emergence of A. mexicana ranged from 3380 to 5302”
Claim · model-written summary
The majority of emergence appeared from March to June (species not restated in the sentence).
Not reportedTaxon
Verbatim
e0“The majority of emergence appeared from March to June”
Claim · model-written summary
Argemone mexicana seeds exhibited dormancy: most germinated in the second season.
- Taxon
- A. mexicanaArgemone mexicanae0
- Outcome
- dormancye0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The seeds of A. mexicana exhibited dormancy, as the majority of seeds germinated in the second season”
Claim · model-written summary
Cumulative growing degree days required for 50% emergence of Brassica tournefortii ranged from 824 to 2311.
- Taxon
- B. tournefortii
Coincya tournefortiie0 - Outcome
- CGDD required for 50% emergencee0
- Value
- 824 to 2311e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The CGDD required for 50% emergence of B. tournefortii ranged from 824 to 2311”
Claim · model-written summary
Most Brassica tournefortii cohorts emerged in the first season from February to June, indicating little seed dormancy.
- Taxon
- B. tournefortiiCoincya tournefortiie0
- Outcome
- appeared in the first seasone0
- Value
- February to Junee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Most cohorts of B. tournefortii appeared in the first season from February to June, indicating little dormancy in seeds”
Claim · model-written summary
Cumulative growing degree days required for 50% emergence of Rapistrum rugosum ranged from 2242 to 2699.
- Taxon
- R. rugosum
Rapistrum rugosume0 - Outcome
- CGDD required for 50% emergencee0
- Value
- 2242 to 2699e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The CGDD required for 50% emergence of R. rugosum ranged from 2242 to 2699”
Claim · model-written summary
The main Rapistrum rugosum cohorts emerged from February to June.
- Taxon
- R. rugosumRapistrum rugosume0
- Outcome
- main cohorts of R. rugosum appearede0
- Value
- February to Junee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The main cohorts of R. rugosum appeared from February to June, and 60% of seeds germinated in the first season, while 40% germinated in the second season”
Claim · model-written summary
About 60% of Rapistrum rugosum seeds germinated in the first season.
- Taxon
- R. rugosumRapistrum rugosume0
- Outcome
- seeds germinated in the first seasone0
- Value
- 60%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The main cohorts of R. rugosum appeared from February to June, and 60% of seeds germinated in the first season, while 40% germinated in the second season”
Claim · model-written summary
About 40% of Rapistrum rugosum seeds germinated in the second season, indicating seed dormancy.
- Taxon
- R. rugosumRapistrum rugosume0
- Outcome
- germinated in the second seasone0
- Value
- 40%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The main cohorts of R. rugosum appeared from February to June, and 60% of seeds germinated in the first season, while 40% germinated in the second season”
Claim · model-written summary
Forecasting emergence of the three weed species from CGDD and rainfall patterns will help growers make better weed management decisions.
Verbatim
e0“forecasting the emergence of three weed species on the basis of CGDD and rainfall patterns will help growers to make better weed management decisions”