Frank Forcella

ENVIRONMENTAL CONTROL OF DORMANCY IN WEED SEED BANKS IN SOIL

Dormancy is a common attribute of many weed seed populations and this usually hampers the task of predicting timing and extent of emergence of weeds. Both the number of established plants and the timing of emergence of a weed are strongly related to the dynamics of dormancy release of the seed population. In this paper, we discuss the different factors that affect dormancy in weed seed banks in soil, aiming to set a conceptual basis that will facilitate the construction of predictive models. From the long list of factors that are known to control dormancy under field conditions, we distinguish those that modify the dormancy level of the population (i.e. soil temperature and soil hydric conditions) from those that terminate dormancy or in other words, remove the ultimate constraints for seed germination once the degree of dormancy is sufficiently low (i.e. light, fluctuating temperatures, nitrate concentration). We also discuss the effect of agricultural practices on dormancy of weed seed populations, making reference to studies that have evinced clearly the factor(s) involved in determining a particular pattern of response. Overall, we stress the importance of clarifying, both qualitatively and quantitatively, the interaction between soil thermal and hydric conditions in the modification of the degree of dormancy of seed populations. Similarly, it is essential that we understand the extent to which such changes in dormancy comprise changes in sensitivity to factors that terminate dormancy. # 2000 Elsevier Science B.V. All rights reserved.

Modeling seedling emergence

Most common approaches to predicting or documenting seedling emergence are imprecise. Mechanistic models that simulate seed dormancy and germination and seedling elongation as functions of measured or estimated environmental variables seem to be the most promising approach to the problem, but they also are the most difficult models to develop. These models will need to integrate soil water potential and soil temperature (hydrothermal time), diurnal soil temperature fluctuations, oxygen deficiency, light quality, and seed burial depth to better describe the direct and interactive effects on and among seed dormancy alleviation and induction, seed germination, and seedling elongation. In the meantime, creation and use of simpler empirical models, which also employ microclimate and soil factors for predictions, may provide sufficiently accurate predictions of seedling emergence until better mechanistic models are developed. Published by Elsevier Science B.V.

RAPID CANOPY CLOSURE FOR MAIZE PRODUCTION IN THE NORTHERN US CORN BELT : RADIATION-USE EFFICIENCY AND GRAIN YIELD

Slow development of maize (Zea tnays L.) canopies in northern areas of the USA may limit light interception and potential productivity. Whether radiation-use efficiency (RUE) and grain yield could be increased by earlier canopy closure was examined with two hybrids contrasting in canopy architecture and potential phytomass production. Early canopy closure was achieved using a combination of row spacings narrower and plant population densities (PPD) greater than typically used by local producers. Maximum interception of incident PAR (0,,,~ x) and total PAR intercepted from sowing to 0m~x(IPAR) increased with PPD. Thermal time to one-half 0n,~(TUo. 0 decreased with increasing PPD. Sowing in narrow (38 cm) rows did not affect 0,,,~. IPAR, or TUn. s in the tall hybrid, Pioneer 3790: nor did it affect grain yield, which increased with PPD up to 10 plants m 2. Grain yield of the dwarf hybrid. SX123, was always less than that of Pioneer 3790. due to its low efficiency in converting intercepted PAR into phytomass. Both hybrids exhibited an optimum rate of canopy development in terms of 0,,,,~. IPAR. and TUo. ~ for grain production. Optima for these parameters varied across years, but were similar for both hybrids and row spacings. These results indicate that hybrids adapted to the northern corn belt may yield more grain if sown at PPDs greater than commonly used to promote early canopy closure. Sowing to rows less than 76 cm wide will have less impact on grain yield. Productivity of hybrids prone to barrenness or with a low efficiency in converting PAR into phytomass, such as SX123, will not improve with earlier canopy closure.

Real-time assessment of seed dormancy and seedling growth for weed management

Computer software called WeedCast was developed to simulate weed seed dormancy, timing of seedling emergence, and seedling height growth in crop environments in real-time and using actual or forecasted weather data. Weather data include daily rainfall and minimum and maximum air temperatures. Air temperatures are converted to average daily soil temperature at 5-cm soil depth using a series of equations that are specific for soil type, tillage system and previous years crop-residue type. Daily rainfall and soil temperature estimates are combined to determine soil water potential (in megapascals) at 5-cm depth. Daily estimated soil water potential or soil temperatures are matched to empirically-derived threshold values that induce secondary dormancy in seeds of certain species. Soil growing degree days (GDD), calculated from soil temperatures, are used to project maximum emergence rates of weed seedlings. Emergence ceases on days when soil water potential falls below threshold values specific to each species. GDD based on air temperatures are used to estimate post-emergence seedling height growth. All three types of simulation provide information that allows users to answer important weed management questions in real-time. These types of questions include but are not limited to the following: (1) Are soil-applied treatments necessary? (2) How late can pre-emergence herbicides be applied? (3) When should mechanical control be implemented? (4) When should field-scouting commence and end? (5) When should post-emergence herbicides be applied?

Spatial and temporal stability of weed populations over five years

Abstract The size, location, and variation in time of weed patches within an arable field were analyzed with the ultimate goal of simplifying weed mapping. Annual and perennial weeds were sampled yearly from 1993 to 1997 at 410 permanent grid points in a 1.3-ha no-till field sown to row crops each year. Geostatistical techniques were used to examine the data as follows: (1) spatial structure within years; (2) relationships of spatial structure to literature-derived population parameters, such as seed production and seed longevity; and (3) stability of weed patches across years. Within years, densities were more variable across crop rows and patches were elongated along rows. Aggregation of seedlings into patches was strongest for annuals and, more generally, for species whose seeds were dispersed by combine harvesting. Patches were most persistent for perennials and, more generally, for species whose seeds dispersed prior to expected dates of combine harvesting. For the most abundant weed in the field, the annual, Setaria viridis, locations of patches in the current year could be used to predict patch locations in the following year, but not thereafter. Nomenclature: Amaranthus retroflexus L. AMARE, redroot pigweed; Asclepias syriaca L. ASCSY, common milkweed; Brassica kaber (DC.) L.C. Wheeler SINAR, wild mustard; Chenopodium album L. CHEAL, common lambsquarters; Cirsium arvense (L.) Scop CIRAR, Canada thistle; Elytrigia repens (L.) Nevski AGRRE, quackgrass; Setaria viridis (L.) Beauv. SETVI, green foxtail; Glycine max (L.) Merr., soybean.

Symposium Integrating measurements of seed availability and removal to estimate weed seed losses due to predation

Abstract To better understand seed predation and enhance weed seed losses in arable fields, we developed a conceptual model that integrates seed dispersal, seed burial, and seed demand, the three processes that determine the dynamics of summer annual weed seeds on the soil surface in late summer and autumn. Published and unpublished experimental data were used to parameterize a simulation model for a number of crop–weed combinations. Sensitivity analyses of models for giant foxtail in corn and soybean indicated that factors related to seed availability were more important in determining overall seed losses due to predation than those related to seed demand. Delaying harvest date and destroying unshed weed seeds collected at harvest emerged as promising strategies to reduce seed input into the seed bank. The role of plant debris in hiding weed seeds from predators was ambiguous and requires further investigation. Estimates of overall seed losses due to predation based on model simulations in various crops and cropping systems indicated that weed seed predation could serve as an important tool in ecological weed management. Nomenclature: Giant foxtail, Setaria faberi Herrm. SETFA; corn, Zea mays L.; soybean, Glycine max (L.) Merr.

Estimating hourly incoming solar radiation from limited meteorological data

Abstract Two major properties that determine weed seed germination are soil temperature and moisture content. Incident radiation is the primary variable controlling energy input to the soil system and thereby influences both moisture and temperature profiles. However, many agricultural field sites lack proper instrumentation to measure solar radiation directly. To overcome this shortcoming, an empirical model was developed to estimate total incident solar radiation (beam and diffuse) with hourly time steps. Input parameters for the model are latitude, longitude, and elevation of the field site, along with daily precipitation with daily minimum and maximum air temperatures. Field validation of this model was conducted at a total of 18 sites, where sufficient meteorological data were available for validation, allowing a total of 42 individual yearly comparisons. The model performed well, with an average Pearson correlation of 0.92, modeling index of 0.95, modeling efficiency of 0.80, root mean square error of 111 W m−2, and a mean absolute error of 56 W m−2. These results compare favorably to other developed empirical solar radiation models but with the advantage of predicting hourly solar radiation for the entire year based on limited climatic data and no site-specific calibration requirement. This solar radiation prediction tool can be integrated into dormancy, germination, and growth models to improve microclimate-based simulation of development of weeds and other plants.

Software Tools for Weed Seed Germination Modeling

Abstract The next generation of weed seed germination models will need to account for variable soil microclimate conditions. To predict this microclimate environment we have developed a suite of individual tools (models) that can be used in conjunction with the next generation of weed seed germination models. The three tools that will be outlined here are GlobalTempSIM, GlobalRainSIM, and the soil temperature and moisture model (STM2). Each model was compared with several sets of observed data from worldwide locations. Overall, the climate predictors compared favorably. GlobalTempSIM had a bias between −2.7 and +0.9 C, mean absolute errors between 1.9 and 5.0 C, and an overall Willmott d-index of 0.79 to 0.95 (where d  =  1 represents total agreement between observed and modeled data) for 12 global validation sites in 2007. GlobalRainSIM had a bias for cumulative precipitation ranging from −210 to +305 mm, a mean absolute error between 29 and 311 mm, and a corresponding d-index of 0.78 to 0.99 for the sites and years compared. The high d-indices indicate that the models adequately captured the annual patterns for the validation sites. STM2 also performed well in comparisons with actual soil temperatures with a range of −2 to +4.6 C biases and mean absolute errors between 0.7 and 6.8 C, with the d-index ranging from 0.83 to 0.99 for the soil temperature comparisons. The soil moisture prediction annual bias was between −0.09 and +0.12 cm3 cm−3, mean absolute errors ranging from 0.02 to 0.16 cm3 cm−3, and possessed a d-index between 0.32 and 0.91 for the validation sites. These models were developed in JAVA, are simple to use, operate on multiple platforms (e.g., Mac, personal computer, Sun), and are freely available for download from the U.S. Department of Agriculture Agricultural Research Service website (http://www.ars.usda.gov/Services/docs.htm?docid=11787).

Environmental factors affecting seed persistence of annual weeds across the U.S. corn belt

Abstract Weed seedbanks have been studied intensively at local scales, but to date, there have been no regional-scale studies of weed seedbank persistence. Empirical and modeling studies indicate that reducing weed seedbank persistence can play an important role in integrated weed management. Annual seedbank persistence of 13 summer annual weed species was studied from 2001 through 2003 at eight locations in the north central United States and one location in the northwestern United States. Effects of seed depth placement, tillage, and abiotic environmental factors on seedbank persistence were examined through regression and multivariate ordinations. All species examined showed a negative relationship between hydrothermal time and seedbank persistence. Seedbank persistence was very similar between the two years of the study for common lambsquarters, giant foxtail, and velvetleaf when data were pooled over location, depth, and tillage. Seedbank persistence of common lambsquarters, giant foxtail, and velvetleaf from October 2001 through 2002 and October 2002 through 2003 was, respectively, 52.3% and 60.0%, 21.3% and 21.8%, and 57.5% and 57.2%. These results demonstrate that robust estimates of seedbank persistence are possible when many observations are averaged over numerous locations. Future studies are needed to develop methods of reducing seedbank persistence, especially for weed species with particularly long-lived seeds. Nomenclature: Common lambsquarters, Chenopodium album L. CHEAL; giant foxtail, Setaria faberi Herrm. SETFA; velvetleaf, Abutilon theophrasti Medik. ABUTH.

A Hydrothermal Seedling Emergence Model for Giant Ragweed (Ambrosia trifida)

Abstract Late-season giant ragweed emergence in Ohio crop fields complicates decisions concerning the optimum time to implement control measures. Our objectives were to develop a hydrothermal time emergence model for a late-emerging biotype and validate the model in a variety of locations and burial environments. To develop the model, giant ragweed seedlings were counted and removed weekly each growing season from 2000 to 2003 in a fallow field located in west central Ohio. Weather data, soil characteristics and geographic location were used to predict soil thermal and moisture conditions with the Soil Temperature and Moisture Model (STM2). Hydrothermal time (θHT) initiated March 1 and base values were extrapolated from the literature (Tb = 2 C, ψb = −10 MPa). Cumulative percent emergence initially increased rapidly and reached 60% of maximum by late April (approximately 400 θHT), leveled off for a period in May, and increased again at a lower rate before concluding in late July (approximately 2,300 θHT). The period in May when few seedlings emerged was not subject to soil temperatures or water potentials less than the θHT base values. The biphasic pattern of emergence was modeled with two successive Weibull models that were validated in 2005 in a tilled and a no-tillage environment and in 2006 at a separate location in a no-tillage environment. Root-mean-square values for comparing actual and model predicted cumulative emergence values ranged from 8.0 to 9.5%, indicating a high degree of accuracy. This experiment demonstrated an approach to emergence modeling that can be used to forecast emergence on a local basis according to weed biotype and easily obtainable soil and weather data. Nomenclature: Giant ragweed, Ambrosia trifida L.