Barney P. Caton

Development and validation of a weed screening tool for the United States

The Australian weed risk assessment has been promoted as a simple and effective screening tool that can help prevent the entry of weeds and invasive plants into new areas. On average, the Australian model identifies major-invaders more accurately than it does non-invaders (90% vs. 70% accuracy). While this difference in performance emphasizes protection, the overall accuracy of the model will be determined by its performance with non-invaders because the frequency of invasive species among new plant introductions is relatively low. In this study, we develop a new weed risk assessment model for the entire United States that increases non-invader accuracy. The new screening tool uses two elements of risk, establishment/spread potential and impact potential, in a logistic regression model to evaluate the invasive/weedy potential of a species. We selected 204 non-invaders, minor-invaders, and major-invaders to develop and validate the new model, and compare its performance to the Australian model using the same set of species. Performing better than the Australian model, our new model accurately identified 94.1% of major-invaders and 97.1% of non-invaders, without committing any false positives or false negatives. The new secondary screening tool we developed reduced the number of species requiring secondary evaluation from 22 to 12%. We expect that the new weed risk assessment model should significantly enhance the United State’s timeliness and accuracy in regulating potential weeds.

A plant growth model for integrated weed management in direct-seeded rice. III. Interspecific competition for light

The model DSRICE1 was developed for analyzing integrated weed management strategies for direct-seeded rice. We have shown that DSRICE1 predicts monoculture rice growth well and accounts for water-depth effects on growth. Here, the model is used to simulate competition for light between rice and two weeds, Echinochloa oryzoides (early watergrass) and Ammannia spp. (redstem). Except for minor differences in phenology, weed growth was simulated as described for rice. Direct competition for light depended on the species’ vertical distributions of leaf and stem areas (live and dead) and their extinction coefficients. Water also attenuates light, so species’ early height growth rates were important because they determined when plants emerged into full light. Structural sensitivity analyses of rice in competition with the two weeds revealed that waterdepth effects and leaf area distributions strongly affected competition, and shading by dead leaf and stem dry mass reduced total production. Validation was based on independent data sets for redstem and watergrass competition using several statistical tests and indices. For rice‐redstem competition, DSRICE1 simulated rice growth well because redstem competitive effects were small, but predictions of redstem growth were good only when observed heights were matched in simulations. Redstem competitiveness depended on height growth rate, perhaps due to its small seed size. For rice‐watergrass competition, the growth of both species was predicted well, except that watergrass growth in plots with early-season drainage was underpredicted. Watergrass parameters were similar to those for rice except for faster height growth and higher photosynthesis rates. In a model application, simulations in which rice seeding was delayed for a time after flooding led to greater yield losses from redstem than from watergrass because delays reduced the advantage of rice over redstem. The usefulness of DSRICE1 for drained fields will be improved by better simulation of plant growth responses to drainage, but rice competition with redstem and watergrass in continuously-flooded fields was simulated well. # 1999 Elsevier Science B.V. All rights reserved.

Ranking Nonindigenous Weed Species by Their Potential to Invade the United States

Abstract Because of the large number of potentially invasive species, and the time required to complete weed risk assessments (WRAs) with the use of the current, mandated system in the United States, species need to be prioritized for assessment and possible listing as Federal Noxious Weeds. Our objective was to rank the potential invasiveness of weedy or pest plant species not yet naturalized in the United States. We created a new model of invasiveness (hereafter the U.S. weed-ranking model) based on scoring factors within four elements: (1) invasiveness potential, or likelihood to exhibit invasive behavior; (2) geographic potential, or habitat suitability; (3) damage potential, or likely impact; and (4) entry potential, or likelihood to be introduced. The ranking score was the product of the four elements. We scored 250 species satisfactorily, from a list of 700 +. We analyzed model sensitivity to scoring factors, and compared results to those from a WRA model for Hawaii. For species not in cultivation in the United States, the top 25 species included a mix of annuals, perennials, sedges, shrubs, and trees. Most had exhibited invasive behavior in at least several other countries. Because of greater entry potential scores, the highest-scoring species were weeds in cultivation. Twenty-nine such species, out of 44 total, had scores greater than the highest scoring species not in cultivation. In comparison to the Hawaii WRA model, correlation and regression analyses indicated that the U.S. weed-ranking model produced similar, but not exact, results. The ranking model differs from other WRAs in the inclusion of entry potential and the use of a multiplicative approach, which better suited our objectives and United States regulations. Two highly ranked species have recently been listed as Federal Noxious Weeds, and we expect most top-tier species to be similarly assessed. Nomenclature: Invasive, exotic weeds, invasiveness model, weed risk assessment

A plant growth model for integrated weed management in direct-seeded rice: I. Development and sensitivity analyses of monoculture growth

A new model, DSRICE1, was developed to analyze weed management strategies in direct-seeded rice (Oryza sativa) systems. Previous rice models have not accounted for important cultural and weed management factors in direct-seeded systems, such as growth from seeds and water-depth effects on plant growth. Here we describe the development and sensitivity analysis of DSRICE1 for monoculture rice growth under water-seeded conditions. DSRICE1 is largely process-based and includes all standard weed management practices except fertility. Simulation inputs include latitude, daily solar radiation, daily maximum and minimum temperatures, water depth, and seed rate. Phenology depends on thermal units. Growth begins with seed storage mobilization to seedlings, and photosynthesis starts when the first leaf is extended. Canopy light dynamics depend on leaf and stem area distributions for both live and dead dry mass, and on water depth when submerged. Water-depth effects were explicitly simulated as reflection and attenuation of light. Model analyses revealed that parameter sensitivities varied over time. Some parameters were always important, while the effects of others were limited to particular parts of the season. Judged over the whole season, the most important parameters were for photosynthesis and light capture. Unlike in most monoculture simulations, early height gain rate was important in DSRICE1 because it determined when plants emerged from the water into full light. Analyses of model structure and specifications revealed that predictions were significantly affected by the use of skewed live leaf area distributions and the non-rectangular hyperbola for the light response curve, and the inclusion of waterdepth and dead canopy dry mass effects on canopy light dynamics. The cropping system and management processes simulated in DSRICE1 had important effects on model predictions of rice growth. Explicit consideration of these factors distinguishes DSRICE1 from other rice growth models, and may lead to better simulation analyses of system interactions with plant growth and weed management strategies. # 1999 Elsevier Science B.V. All rights reserved.

A plant growth model for integrated weed management in direct-seeded rice II. Validation testing of water-depth effects and monoculture growth

Manipulating water depths and timing is a key management practice in rice cropping systems, but rice models have not simulated water-depth effects on plant growth. A new plant growth model, DSRICE1, simulates most cultural and weed management practices except fertility. Water-depth effects on plant light capture are mechanistically simulated as reflection and attenuation of light by water. Light attenuation by water in the model depended on water depth and the light extinction coefficient of water, kH2O (m ˇ1 ). DSRICE1 was validation tested for prediction of monoculture growth, and specifically for early-season water-depth effects. Analyses revealed that attenuation by water limited irradiance and reduced seedling growth, and indicated which plant traits contributed to growth during submergence. Reflection did not affect rice growth and may be ignored. In empirical validation tests, DSRICE1 predictions were compared with data from 14 independent data sets. For growth up to 40 days after seeding (DAS) (11 experiments), simulations without water-depth effects failed validation tests by overpredicting rice shoot dry mass (DM). In contrast, DSRICE1 simulations with water-depth effects included were accurate with kH2O from 1 to 4, kH2Oa 3 was best overall, and predictions improved with experiment-specific kH2O values. DSRICE1 also accurately predicted differences in shoot DM per plant in a 1985 water-depth study, while again simulations without water-depth effects did not. Thus, water-depth effects were required to simulate early rice growth accurately. In whole-season empirical validation tests (14 experiments), DSRICE1 simulated shoot, stem, leaf, live leaf, and live stem DM accurately, especially considering the range of data used and the fact that no calibration was needed. DSRICE1 was also corroborated by the fact that it accounts for many physical factors and plant traits that affect submergence tolerance. The model or the techniques it uses may be useful in analyses of cultivar tolerance to submergence. In some respects, DSRICE1 was subjected to more rigorous validation testing than previous rice models, and potentially explains more interactions between rice and weed growth and management. This approach may broaden and improve simulation analyses of integrated weed management in direct-seeded rice systems. # 1999 Elsevier Science B.V. All rights reserved.

A temperature-based model of direct-, water-seeded rice (Oryza sativa) stand establishment in California

We used data sets from 23 field and greenhouse experiments to develop, validate, and evaluate a temperature-based model of stand establishment for direct-, water-seeded rice (Oryza sativa) culture. Data were split for model estimation and validation. The best performing stand density model used seed rate and total degree–days (adjusted R2=65%). We detected no problems with this models assumptions or statistical adequacy, and alternate models performed worse or were invalidated. The cultivars used in validation account for almost 75% of the rice currently grown in California. The model is therefore widely applicable and is useful either alone or as a submodel in rice growth and management models. Using long-term degree–day averages for California rice growing areas, we evaluated the effects of planting dates and rates on achieving optimal stand densities. These results showed why typical grower practices have been successful, but suggested ways that planting date and seeding rates might be adjusted to increase stand densities when weed problems are anticipated.

California arrowhead is a weak competitor in water-seeded rice

Abstract California arrowhead is a broadleaf weed widespread in water-seeded rice. Bensulfuron is the only herbicide currently available for use throughout the California rice growing region that provides complete control of California arrowhead; however, resistance to bensulfuron has been detected in California arrowhead and in several other weed species. Growers have herbicide alternatives for weed species other than California arrowhead but continue to use bensulfuron year after year for control because they believe California arrowhead reduces rice yields. However, damage thresholds have not been determined for this weed, and the crop may be able to tolerate relatively high California arrowhead densities. In this work, the damage thresholds for California arrowhead were determined in field and greenhouse experiments. Water-seeded rice was grown in mixture with California arrowhead in a 1992 greenhouse experiment and in field experiments in 1992 and 1998. Rice tiller density and grain yields were not affected by California arrowhead densities up to 200 plants m−2 in any year. Rice was taller than California arrowhead throughout the growing season in all experiments, and the weed senesced well before rice maturity. The ability of the crop to overtop the weed and grow weed-free during the latter part of the season may explain why California arrowhead is such a weak competitor with water-seeded rice. The results suggest that growers may be able to tolerate California arrowhead densities up to 200 plants m−2 without detectable yield losses. Implications for weed management are discussed. Nomenclature:Bensulfuron, California arrowhead, Sagittaria montevidensis Cham. and Schlect SAGMO; rice, Oryza sativa L.