Potential yield loss in grain sorghum (<i>Sorghum bicolor</i>) with weed interference in the United States

Dille, J. Anita; Stahlman, Phillip W.; Thompson, Charles P.; Bean, Brent W.; Soltani, Nader; Sikkema, Peter H.

doi:10.1017/wet.2020.12

Cited by 25 publications

(33 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Graham et al [44] reported that high weed density negatively affected leaf area and light absorption, resulting in affected crop yield. These results support Dille et al [45], who reported that approximately 47% of sorghum total yield was lost due to weed interference.…”

Section: Discussionsupporting

confidence: 91%

Sweet Sorghum (Sorghum bicolor) Performance in a Legume Intercropping System under Weed Interference

2021

View full text Add to dashboard Cite

Sweet sorghum (Sorghum bicolor L. Moench) is highly susceptible to weed competition during the early growth stages; hence, intercropping is considered to overcome the weed competition challenge. This study was conducted to determine the performance of sweet sorghum in legume intercropping systems under different weed management pressures. Three cropping systems (sole crop, inter-row, and intra-row intercropping) and three weed management levels (no weeding after crop emergency, ceasing weeding 50 days after crop emergency, and weeding throughout) were tested. Intercropping pattern had a significant (p < 0.05) impact on the plant and the number of leaves per plant, while other treatments remained insignificant during the 2017/18 growing season. During the 2018/19 growing season, the intercropping pattern had a significant (p < 0.05) effect on dry and fresh biomass and plant height at 60 days after emergence. An increase in weeding frequencies reduced Brix (◦Bx). Uncontrolled weed plots had the lowest sweet sorghum dry biomass accumulation, whereas the biomass increased as weeding frequencies increased but remained insignificant as weeding frequencies further increased from 50% to 100% in both seasons. Consequently, SS/DB intra-row intercropping and intermediate weeding are sufficient for optimum SS biomass production and sugar levels.

show abstract

Section: Discussionsupporting

confidence: 91%

Sweet Sorghum (Sorghum bicolor) Performance in a Legume Intercropping System under Weed Interference

2021

View full text Add to dashboard Cite

show abstract

“…Yield losses from 37% to 61% were reported by Dille, et al [1] in grain sorghum with weed interference in different regions of the United States. The results from field studies showed that growers in different regions of the United States and Canada would potentially lose an average of 31% to 94% of their dry bean yield [2] and 61% to 83% of the sugar beet yield [3] due to weeds.…”

Section: Introductionmentioning

confidence: 90%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

View full text Add to dashboard Cite

Crop and weeds identification is of important steps towards the development of efficient automotive weed control systems. The higher the accuracy of plant detection and classification, the higher the performance of the weeding machine. In this study, the capability of two popular boosting methods including Adaboost.M1 and LogitBoost algorithms was evaluated to enhance the plant classification performance of four classifiers, namely Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Random Forest (RF), and Support Vector Machine (SVM). Four feature filtering techniques including Correlationbased Feature Selection (CFS), Information Gain (IG), Gain Ratio (GR), and OneR were applied to the imageextracted features and 10 of the most significant features were selected and fed into single and boosted classifiers. The RF model trained by IG selected features (IG-RF) was the most appropriate classifier among the evaluated models whether in single or boosted modes. It was also found that boosting of IG-RF by using Adaboost.M1 and LogitBoost algorithms improved the classification accuracy. Regarding the performance values, the LogitBoost-IG-RF structure, which provided a classification accuracy of 99.58%, a kappa (k) of 0.9948, and a Root Mean Squared Error (RMSE) of 0.0688 on training dataset, was selected as the most appropriate classifier for plant discrimination in peanut fields. The accuracy, k, and RMSE criteria of this combination on test dataset were 95.00%, 0.9375, and 0.1591, respectively. It was concluded that combination of boosting algorithms and feature selection methods can promote plant type discrimination accuracy, which is a crucial factor in the development of precision weed control systems.

show abstract

“…verticilliflorum [46]. While sudangrass can be grown as a forage crop, shattercane is a weedy relative of sorghum that is a considered a serious weed whose agronomic importance has increased over the years [14,26,27,31,46,[49][50][51].…”

Section: Originmentioning

confidence: 99%

“…All reports of yield loss due to shattercane competition are from field trials conducted in the United States, with the exception of the case study by Raey et al [27], which was conducted in Iran, Asia (Table 1). Especially in the United States, shattercane is one of the most common and problematic weeds in grain sorghum [50]. There are also case studies from this continent showing the competitive ability of shattercane against domesticated sorghum and other important summer field crops.…”

Section: Competitive Ability and Distributionmentioning

confidence: 99%

Shattercane (Sorghum bicolor (L.) Moench Subsp. Drummondii) and Weedy Sunflower (Helianthus annuus L.)—Crop Wild Relatives (CWRs) as Weeds in Agriculture

et al. 2021

View full text Add to dashboard Cite

Shattercane (Sorghum bicolor (L.) Moench subsp. drummondii) and weedy sunflower (Helianthus annuus L.) are two examples of crop wild relatives (CWRs) that have become troublesome weeds in agriculture. Shattercane is a race belonging to a different subspecies than domesticated sorghum (Sorghum bicolor (L.) Moench subsp. bicolor). Weedy sunflower populations are natural hybrids between wild and domesticated sunflower (Helianthus annuus L.). Both species have key weedy characteristics, such as early seed shattering and seed dormancy, which play an important role in their success as agricultural weeds. They are widely reported as important agricultural weeds in the United States and have invaded various agricultural areas in Europe. Shattercane is very competitive to sorghum, maize (Zea mays L.), and soybean (Glycine max (L.) Merr.). Weedy sunflower causes severe yield losses in sunflower, maize, soybean, pulse crops, and industrial crops. Herbicide resistance was confirmed in populations of both species. The simultaneous presence of crops and their wild relatives in the field leads to crop–wild gene flow. Hybrids are fertile and competitive. Hybridization between herbicide-tolerant crops and wild populations creates herbicide-resistant hybrid populations. Crop rotation, false seedbed, cover crops, and competitive crop genotypes can suppress shattercane and weedy sunflower. Preventative measures are essential to avoid their spread on new agricultural lands. The development of effective weed management strategies is also essential to prevent hybridization between sorghum, sunflower, and their wild relatives and to mitigate its consequences.

show abstract

Potential yield loss in grain sorghum (Sorghum bicolor) with weed interference in the United States

Cited by 25 publications

References 21 publications

Sweet Sorghum (Sorghum bicolor) Performance in a Legume Intercropping System under Weed Interference

Sweet Sorghum (Sorghum bicolor) Performance in a Legume Intercropping System under Weed Interference

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Shattercane (Sorghum bicolor (L.) Moench Subsp. Drummondii) and Weedy Sunflower (Helianthus annuus L.)—Crop Wild Relatives (CWRs) as Weeds in Agriculture

Contact Info

Product

Resources

About