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Spatial distribution of broadleaf weeds within 14 North Carolina soybean fields was characterized by fitting negative binomial distributions to frequency distributions of weed counts in each field. In most cases, the data could be represented by a negative binomial distribution. Estimated values of the parameter K of this distribution were small, often less than one, indicating a high degree of patchiness. The data also indicated that the population as a whole was patchy. Counts of individual species were positively correlated with each other in some fields and total weed count could be represented by a negative binomial for 12 of the 14 fields.
Broadleaf weeds apparently have patchy distributions within a field while POST control decisions are made assuming a regular spatial distribution. As a result, yield loss from weed competition may be overestimated, possibly leading to mistakes in choosing the optimal control treatment. Data on distribution of broadleaf weeds in 14 soybean fields were used in simulation experiments to investigate the potential for improving decision making with information about weed patchiness. The feasibility of modeling weed distribution in individual fields was also examined. Overall, the cost of assuming a regular distribution when making POST decisions was found to be low. Errors that occurred most often involved recommending more intensive control than was actually required, although in a few cases less intensive control was recommended. Error in the yield loss estimated for the uncontrolled population did not indicate the potential for a mistake in decision making for a field. Accurately modeling distribution of weeds within fields may be difficult as a result of correlations between distributions of individual species within a field and variation in distributions between fields.
A soil sampler, elutriator, and associated sample flushing device were designed and constructed for an intensive study of weed seedbanks. This equipment was used in 1993 to collect and process 4980 soil samples. The sampler was durable, core size was consistent, and sampling was efficient. Cores were approximately 200 cm3 and two people could take 120 cores/h. The elutriator separated weed seeds from 36 of these cores at a time. Washing required 60 to 75 min depending on soil texture. Seeds as small as 0.3 mm in diam were recovered and almost 100% of the seeds were recovered from samples spiked with barnyardgrass, redroot pigweed, velvetleaf, and witchgrass. The flushing device was used to transfer sample contents from strainers of the elutriator to propyltex bags for drying and storing. Equipment like this, plus improved technology for identifying and counting seeds, is needed to make weed seedbank studies more feasible.
Bioeconomic weed management models help growers achieve appropriate weed management with less herbicide by matching management to the weed population in a field. Growers, however, will not use bioeconomic models unless cost-effective methods to sample their weed populations are identified. Counting and identifying seeds and seedlings is the most time-consuming and costly part of sampling weed populations. The time required for this process was investigated and modeled as a first step in developing sampling plans for growers using WEEDCAM, a bioeconomic model for weed management in Zea mays L. in Colorado. The time required to count and identify seeds or seedlings was recorded for 9,405 soil cores (5 cm in diameter and 10 cm deep) and 9,726 quadrats (18-cm band over 1.52 m of crop row) collected or examined in eight corn fields in eastern Colorado. The time required to count and identify seeds was best described using a log-linear regression with time increasing with the number of seeds and species and the amount of sand in the core. The average cost of determining there are no seeds in a core is $1.07 for a core from a field with 37% sand and $4.32 if the field has 88% sand. The average cost of counting and identifying 36 seeds of four species is $2.70 and $10.88 for cores with 37 and 88% sand, respectively. The time required to count and identify seedlings was best described using a log-linear regression with time increasing with the number of seedlings and species. Classifying seedlings as grass or broadleaf did not improve the model. The average cost of determining that a quadrat is weed-free is $0.02. The average cost of counting and identifying 37 seedlings is $0.05, $0.07, $0.19, and $0.26 per quadrat for 1, 2, 4, and 6 species, respectively. The cost of identifying seeds and seedlings in eastern Colorado Z. mays fields to use WEEDCAM is estimated as $2.71 per core for the seed bank and $0.08 per quadrat for seedlings.
An integrated approach to weed management in pinto bean is needed since available herbicides seldom adequately control all weed species present in a field. A two-year study was conducted to assess weed control efficacy and pinto bean tolerance to mechanical weeding from a rotary hoe or flex-tine harrow at crook, unifoliolate, and trifoliolate stages of bean development. Weed control was similar for both implements and all timings in 1993. In 1994, mechanical weeding at trifoliolate and both crook and trifoliolate stages controlled more weeds than at other growth stages, regardless of type of implement. Using the flex-tine harrow reduced pinto bean stand, but results based on growth stage were not consistent each year. Damage to pinto bean hypocotyls and stems was observed with the flex-tine harrow used at both crook and trifoliolate stages in 1994. Rotary hoeing did not reduce pinto bean stand or cause injury. Yield and seed weight did not differ among treatments in either year.
Effectiveness of rotary hoeing with cultivation and comparison of an in-row cultivator with a standard row-crop cultivator were determined in dry edible bean. The effectiveness of in-row cultivation conducted at various timings and frequencies was examined. The in-row cultivator was more effective in reducing weed populations than the standard cultivator, although at least two mechanical weeding operations were needed to reduce weed populations to levels of the herbicide check (EPTC [S-ethyl dipropyl carbamothioate] plus ethalfluralin). When the in-row cultivation was delayed until the second trifoliolate stage or later, weed populations were greater than those in the herbicide check. In situations with high weed populations, rotary hoeing prior to cultivation was required to reduce weed populations to levels similar to the herbicide check. An in-row cultivator has potential to improve mechanical weed control options in a crop such as dry edible bean. The types of adjustments made in combination with soil textures, soil moisture, and operator experience affect overall weed control. Thus, it is expected that the level of weed control will vary from year to year and even field to field for the same operator.
Process-level simulation models can be a unique and effective medium for teaching concepts of crop biology and management which can usually only be studied in the field. WEEDING was developed from several research models to demonstrate concepts of weed ecology, herbicide efficacy and selectivity, and economic thresholds for weed control decisions. Users of WEEDING interactively grow a soybean crop, making management decisions throughout the seaason. The program includes real weather data and a realistic assortment of soybean varieties, soil types, weed species, and weed control options. Production costs and profit are calculated at the end of a simulated season. The structure of a process-level model driven by real weather data simulates the risk involved in management decisions.
Knowing the distribution of weed seedlings in farmer-managed fields could help researchers develop reliable distribution maps for site-specific weed management. With a knowledge of the spatial arrangement of a weed population, cost effective sampling programs and management strategies can be designed, so inputs can be selected and applied to specific field areas where management is warranted. In 1997 and 1998, weeds were sampled at 612 to 682 sites in two center pivot irrigated corn fields (71 and 53 ha) in eastern Colorado. Weeds were enumerated when corn reached the two-leaf, four-leaf, and physiological maturity stages in a 76.2- by 76.2-m grid, a random-directed grid where sites were established at intervals of 76.2 m, and a star configuration based on a 7.62- by 7.62-m grid within three 23,225 m2 areas. Directional correlograms were calculated for 0, 30, 60, 90, 120, and 150° from the crop row. Fifteen weed species were observed across fields. Spatial dependence occurred in 7 of the 93 samples (a collection of sampling units for a particular weed species that was detected within a field at a particular sampling time and year) for populations of field sandbur, pigweed species, nightshade species, and common lambsquarters. Correlogram analysis indicated that 18 to 72% of the variation in sample density was a result of spatial dependence over a geographic distance not exceeding 5 to 363 m among the examined data. Because of the lack of spatial correlation for weed seedling distributions in these eastern Colorado corn fields, interpolated density maps should be based on grid sizes (separation distances) less than 7.62 m for weed seedling infestations.
Studies quantifying weed seed production as a function of weed density are expensive and difficult, and lack of these data is a common limitation in modeling weed population dynamics over time. Observed empirical and theoretical relationships between crop yield loss curves and weed seed production curves led us to the hypothesis that there should be a strong relationship between the shapes of these two curves. Data from literature sources were evaluated to test this hypothesis for hyperbolic curves and to determine if the data describing the crop yield loss caused by weeds could provide estimates of the shape parameter of a hyperbolic equation for describing density dependence in weed reproduction. For each of 162 data sets, a shape parameter (N50) and a scale parameter (U) were estimated for an increasing hyperbolic model both for absolute crop yield loss as a function of weed density (N50YL, UYL) and for weed yield (either total biomass yield or seed yield) as a function of weed density (N50WY, UWY). N50YL was strongly correlated with N50WY across all data sets, with an apparent 1:1 relationship between the two. This relationship suggests that the shape parameter of the yield loss model may substitute for the shape parameter of a hyperbolic model describing the density-dependence of weed seed production. This substitution will be most useful in weed population modeling situations where data describing crop yield loss as a function of weed density are already available, but data describing weed seed production as a function of weed density are not available.
Growers need affordable methods to sample weed populations to reduce herbicide use with site-specific weed management. Sampling programs and methods of developing sampling programs for integrated pest management are not sufficient for site-specific weed management because more and different information is needed to make treatment maps than simply estimate average pest density. Sampling plans for site-specific weed management must provide information to map the weeds in the field but should be developed for the objective of prescribing spatially variable management. Weed scientists will be most successful at designing plans for site-specific weed management if they focus on this objective throughout the process of designing a sampling plan. They must also learn more about the spatial distribution and dynamics of weed populations and use that knowledge to identify cost-effective plans, recommend methods to make maps as well as collect data, and find ways to evaluate maps that reflect management to be prescribed from the map. Foremost, sampling must be thought of as an ongoing process over time that uses many types of information rather than a single event of collecting one type of information. Specifically, scientists will need to identify common characteristics rather than just differences of the spatial distribution of weeds among fields and species, recognize that map accuracy may be a poor indicator of the value of a sampling plan, and develop methods to use growers' knowledge of the distribution of weeds and past spatially variable management within a field for both making a map and recommending a sampling plan. The value of proposed methods for sampling and mapping must also be demonstrated or adoption of site-specific weed management might be limited to growers who enjoy using sophisticated technology.
The use of scouting and economic thresholds has not been accepted as readily for managing weeds as it has been for insects, but the economic threshold concept is the basis of most weed management decision models available to growers. A World Wide Web survey was conducted to investigate perceptions of weed science professionals regarding the value of these models. Over half of the 56 respondents were involved in model development or support, and 82% thought that decision models could be beneficial for managing weeds, although more as educational rather than as decision-making tools. Some respondents indicated that models are too simple because they do not include all factors that influence weed competition or all issues a grower considers when deciding how to manage weeds. Others stated that models are too complex because many users do not have time to obtain and enter the required information or are not necessary because growers use a zero threshold or because skilled decision makers can make better and quicker recommendations. Our view is that economic threshold–based models are, and will continue to be, valuable as a means of providing growers with the knowledge and experience of many experts for field-specific decisions. Weed management decision models must be evaluated from three perspectives: biological accuracy, quality of recommendations, and ease of use. Scientists developing and supporting decision models may have hindered wide-scale acceptance by overemphasizing the capacity to determine economic thresholds, and they need to explain more clearly to potential users the tasks for which models are and are not suitable. Future use depends on finding cost-effective methods to assess weed populations, demonstrating that models use results in better decision making, and finding stable, long-term funding for maintenance and support. New technologies, including herbicide-resistant crops, will likely increase rather than decrease the need for decision support.
Plot-scale field studies were conducted to evaluate the efficacy of steam for the control of cropland weeds in comparison with common herbicides. Weed densities, biomass, or emergence after treatment were measured. Steam (3,200 kg/ha, energy dosage equivalent to 890 kJ/m2, speed of 0.8 m/s) and glyphosate (560 g ai/ha) gave similar control (> 90%) of seedling common lambsquarters and seedling redroot pigweed. Applied at heading, steam was comparable to glyphosate in reducing green foxtail biomass at heading 2 wk after application. Steam applied at a rate of 3,200 kg/ha significantly reduced weed biomass (mixed stand, treated at seedling stage) 9 wk after application compared with the control, whereas steam applied at a rate of 1,600 kg/ha (1.6 m/s) did not. Biomass of downy brome treated with steam was reduced more at anthesis than at the seedling growth stage. Emergence of common lambsquarters, redroot pigweed, and black nightshade was not affected by steam application. Amount of steam applied, weed species, and growth stage are key factors in determining control effectiveness.
There are many reasons why agricultural researchers carefully evaluate approaches to experimental data analysis. Agricultural experiments are typically highly complex, with many types of variables often collected at a wide range of temporal and spatial scales. Furthermore, research in the developing world is often conducted on-farm where simple and conventional experimental designs are often unsuitable. Recently, a variant of stochastic dominance called stochastic efficiency with respect to a function (SERF) has been developed and used to analyse long-term experimental data. Unlike traditional stochastic dominance approaches, SERF uses the concept of certainty equivalents (CEs) to rank a set of risk-efficient alternatives instead of finding a subset of dominated alternatives. This study evaluates the efficacy of the SERF methodology for analysing conventional and conservation tillage systems using 14 years (1990–2003) of economic budget data collected from 36 experimental plots at the Iowa State University Northeast Research Station near Nashua, IA, USA. Specifically, the SERF approach is used to examine which of two different tillage systems (chisel plough and no-till) on continuous corn (Zea mays) and corn/soyabean (Glycine max) rotation cropping systems are the most risk-efficient in terms of maximizing economic profitability (gross margin and net return) by crop across a range of risk aversion preferences. In addition to the SERF analysis, we also conduct an economic analysis of the tillage system alternatives using mean-standard deviation and coefficient of variation for ranking purposes. Decision criteria analysis of the economic measures alone provided somewhat contradictive and non-conclusive rankings, e.g. examination of the decision criteria results for gross margin and net return showed that different tillage system alternatives were the highest ranked depending on the criterion and the cropping system (e.g. individual or rotation). SERF analysis results for the tillage systems were also dependent on the cropping system (individual, rotation or whole-farm combined) and economic outcome of interest (gross margin or net return) but only marginally on the level of risk aversion. For the individual cropping systems (continuous corn, rotation corn and rotation soyabean), the no-till tillage and rotation soyabean system was the most preferred and the chisel plough tillage and continuous corn system the least preferred across the entire range of risk aversion for both gross margin and net return. The no-till tillage system was preferred to the chisel plough tillage system when ranking within the continuous corn and the corn-soyabean rotation cropping systems for both gross margin and net return. Finally, when analysing the tillage system alternatives on a whole-farm basis (i.e. combined continuous corn and corn-soybean rotation), the no-till tillage system was clearly preferred to the chisel plough tillage system for both gross margin and net return. This study indicates that the SERF method appears to be a useful and easily understood tool to assist farm managers, experimental researchers and, potentially, policy makers and advisers on problems involving agricultural risk.
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