Herbicide-resistant Grain Sorghum

A fluazifop-resistant sorghum cultivar designated ‘21534_ACCase-R’ and plants comprising a polynucleotide encoding the polypeptide of SEQ ID NO: 39 are disclosed herein. The present invention provides seeds, plants, and plant parts derived from sorghum cultivar ‘21534_ACCase-R’ and those including SEQ ID NO: 39. Further, it provides methods for producing a sorghum plant by crossing ‘21534_ACCase-R’ with itself or another sorghum variety. The invention also encompasses any sorghum seeds, plants, and plant parts produced by the methods disclosed herein, including those in which additional traits have been transferred into ‘21534_ACCase-R’ through the introduction of a transgene or by breeding ‘21534_ACCase-R’ with another sorghum cultivar

Herbicide-resistant Grain Sorghum

A fluazifop-resistant sorghum cultivar designated ‘21534_ACCase-R’ and plants comprising a polynucleotide encoding the polypeptide of SEQ ID NO: 39 are disclosed herein. The present invention provides seeds, plants, and plant parts derived from sorghum cultivar ‘21534_ACCase-R’ and those including SEQ ID NO: 39. Further, it provides methods for producing a sorghum plant by crossing ‘21534_ACCase-R’ with itself or another sorghum variety. The invention also encompasses any sorghum seeds, plants, and plant parts produced by the methods disclosed herein, including those in which additional traits have been transferred into ‘21534_ACCase-R’ through the introduction of a transgene or by breeding ‘21534_ACCase-R’ with another sorghum cultivar

Seed production of barnyardgrass (Echinochloa crus-galli) in response to time of emergence in cotton and rice

The spread of herbicide resistance in barnyardgrass (Echinochloa crus-galli (L.) Beauv.) poses a serious threat to crop production in the southern United States. A thorough knowledge of the biology of barnyardgrass is fundamental for designing effective resistance-management programmes. In the present study, seed production of barnyardgrass in response to time of emergence was investigated in cotton and rice, respectively, in Fayetteville and Rohwer, Arkansas, over a 2-year period (2008–09). Barnyardgrass seed production was greater when seedlings emerged with the crop, but some seed production was observed even if seedlings emerged several weeks after crop emergence. Moreover, barnyardgrass seed production was highly variable across environments. When emerging with the crop (0 weeks after crop emergence (WAE)), barnyardgrass produced c. 35 500 and 16 500 seeds/plant in cotton, and c. 39 000 and 2900 seeds/plant in rice, in 2008 and 2009, respectively. Seed production was observed when seedlings emerged up to 5 WAE (2008) or 7 WAE (2009) in cotton and up to 5 WAE (2008, 2009) in rice; corresponding seed production was c. 2500 and 1500 seeds/plant in cotton, and c. 14 700 and 110 seeds/plant in rice, in 2008 and 2009, respectively. The results suggest that cultural approaches that delay the emergence of barnyardgrass or approaches that make the associated crop more competitive will be useful in integrated management programmes. In the context of herbicide resistance management, it may be valuable to prevent seed return to the seedbank, irrespective of cohorts. The findings are vital for parameterizing herbicide resistance simulation models for barnyardgrass

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April±October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for withinseason data collection of agricultural crops such as sorghum

Modelling the Dynamics of Feral Alfalfa Populations and Its Management Implications

BACKGROUND: Feral populations of cultivated crops can pose challenges to novel trait confinement within agricultural landscapes. Simulation models can be helpful in investigating the underlying dynamics of feral populations and determining suitable management options. METHODOLOGY/PRINCIPAL FINDINGS: We developed a stage-structured matrix population model for roadside feral alfalfa populations occurring in southern Manitoba, Canada. The model accounted for the existence of density-dependence and recruitment subsidy in feral populations. We used the model to investigate the long-term dynamics of feral alfalfa populations, and to evaluate the effectiveness of simulated management strategies such as herbicide application and mowing in controlling feral alfalfa. Results suggest that alfalfa populations occurring in roadside habitats can be persistent and less likely to go extinct under current roadverge management scenarios. Management attempts focused on controlling adult plants alone can be counterproductive due to the presence of density-dependent effects. Targeted herbicide application, which can achieve complete control of seedlings, rosettes and established plants, will be an effective strategy, but the seedbank population may contribute to new recruits. In regions where roadside mowing is regularly practiced, devising a timely mowing strategy (early- to mid-August for southern Manitoba), one that can totally prevent seed production, will be a feasible option for managing feral alfalfa populations. CONCLUSIONS/SIGNIFICANCE: Feral alfalfa populations can be persistent in roadside habitats. Timely mowing or regular targeted herbicide application will be effective in managing feral alfalfa populations and limit feral-population-mediated gene flow in alfalfa. However, in the context of novel trait confinement, the extent to which feral alfalfa populations need to be managed will be dictated by the tolerance levels established by specific production systems for specific traits. The modelling framework outlined in this paper could be applied to other perennial herbaceous plants with similar life-history characteristics

Simulation models on the ecology and management of arable weeds : structure, quantitative insights, and applications

Fil: Bagavathiannan, Muthukumar V. Texas A and M University. Department of Soil and Crop Sciences. College Station. USA.Fil: Beckie, Hugh J. The University of Western Australia. School of Agriculture and Environment. Western Australia, Australia.Fil: Chantre, Guillermo R. Universidad Nacional del Sur. Departamento de Agronomía. CERZOS. Bahía Blanca, Buenos Aires, Argentina. - CONICET - Universidad Nacional del Sur. Departamento de Agronomía. CERZOS. Bahía Blanca, Buenos Aires, Argentina.Fil: González Andújar, José L. Instituto de Agricultura Sostenible (CSIC). Cordoba, Spain.Fil: León, Ramón G. North Carolina State University. Department of Crop and Soil Sciences. Center for Environmental Farming Systems, Genetic Engineering and Society Center. Raleigh, USA.Fil: Neve, Paul. Agriculture and Horticulture Development Board. Stoneleigh Park, Kenilworth, UK.Fil: Poggio, Santiago Luis. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. - CONICET – Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Buenos Aires, Argentina. - Universidad de Buenos Aires. Facultad de Agronomía. Departamento de Producción Vegetal. Cátedra de Producción Vegetal. Buenos Aires, Argentina.Fil: Schutte, Brian J. New Mexico State University. Department of Entomology, Plant Pathology and Weed Science. Las Cruces, USA.In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.grafs

Simulation models on the ecology and management of arableweeds: Structure, quantitative insights, and applications

In weed science and management, models are important and can be used to better understand what has occurred in management scenarios, to predict what will happen and to evaluate the outcomes of control methods. To-date, perspectives on and the understanding of weed models have been disjointed, especially in terms of how they have been applied to advance weed science and management. This paper presents a general overview of the nature and application of a full range of simulation models on the ecology, biology, and management of arable weeds, and how they have been used to provide insights and directions for decision making when long-term weed population trajectories are impractical to be determined using field experimentation. While research on weed biology and ecology has gained momentum over the past four decades, especially for species with high risk for herbicide resistance evolution, knowledge gaps still exist for several life cycle parameters for many agriculturally important weed species. More research efforts should be invested in filling these knowledge gaps, which will lead to better models and ultimately better inform weed management decision making.Fil: Bagavathiannan, Muthukumar V.. Texas A&M University; Estados UnidosFil: Beckie, Hugh J.. University of Western Australia; AustraliaFil: Chantre Balacca, Guillermo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida. Universidad Nacional del Sur. Centro de Recursos Naturales Renovables de la Zona Semiárida; Argentina. Universidad Nacional del Sur. Departamento de Agronomía; ArgentinaFil: González Andujar, José L.. Consejo Superior de Investigaciones Científicas; EspañaFil: Leon, Ramon G.. North Carolina State University; Estados UnidosFil: Neve, Paul. Agriculture & Horticulture Development Board; Reino UnidoFil: Poggio, Santiago Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura; ArgentinaFil: Schutte, Brian J.. New Mexico State University.; Estados UnidosFil: Somerville, Gayle J.. Sustainable Agriculture Sciences; Reino UnidoFil: Werle, Rodrigo. University of Wisconsin; Estados UnidosFil: Acker, Rene Van. University of Guelph; Canad