- Agronomic: wheat
- Crop Production: conservation tillage
- Education and Training: on-farm/ranch research
- Farm Business Management: whole farm planning
- Pest Management: integrated pest management, weed ecology
- Production Systems: agroecosystems, transitioning to organic
The negative effects of agriculture have become a growing concern. The goals of this study were 1) to increase our understanding of the importance of the distribution and diversity of weed communities in conventional and organic spring wheat systems, 2) to determine if these differences have an impact on yield, and 3) determine if it is possible to employ weed community structure as a sustainable management alternative. We found that organic and conventional system weed communities differ in both diversity and spatial arrangement, and that there is reason to believe that these inherent differences may have effects on crop yield.
Agriculture is one of the most integral pieces in a functional society. It is also the largest industry on the planet (Clay, 2004). The ability of agricultural systems to produce adequate food has, in general, grown in parallel with an expanding world population (Tilman et al., 2002; Trewavas, 2002). In recent years, however, the negative effects of agriculture, such as soil and water contamination and erosion, have become a growing concern (Tilman, 1999; van der Werf and Petit, 2002; Biao et al., 2003). In order to meet the needs of the future, how can we continue to increase agricultural production without increasing the negative impacts of intensive agriculture?
In the United States, small farms tend to be more productive, per unit of land, than large farms (Clay, 2004). Farming small areas is, however, problematic for the farmer in that it is difficult to generate enough income with small farming operations (Clay, 2004). Thus, anything that decreases farm cost to the producer will increase the likelihood that small farming operations can adequately support the farmer. This, in turn, could lead to an efficiency increase if it became economically feasible for farming operations in the US to decrease in size.
Weed management using herbicides or cultivation often represents a large cost to farmers. Integrated weed management systems that combine biological, cultural, mechanical, and chemical control methods, have been identified as an alternative form of management that might lead to more sustainable systems (Clements et al., 1994) since they decrease costs to both the farmer and the environment. However, there are still knowledge gaps relating to ecological processes that could lead to the development of practical integrated weed management programs in agricultural systems. For example, relatively little is known about the potential importance of weed community characteristics under different agricultural management systems. Few studies, if any, have investigated the implications of differences in weed communities on crop plants and weed control. Diversity has been proposed as a critical component of stability in natural systems, allowing for nutrient accumulation and increases in productivity over time (Tilman et al., 1997). Would this apply in agricultural systems as well? This study examined two main components of the weed communities of conventional and organic spring wheat systems (spatial patterns and diversity) and attempted to relate these to effects on spring wheat productivity.
Spatial Patterns of Weed Communities
Knowledge of the spatial distribution of weeds within a field is useful for determining where and when to carry out weed control (Thornton et al., 1990; Wiles et al., 1992) and in making accurate estimates of yield loss due to weeds (Brain and Cousens, 1990; Thornton et al., 1990). Up to this point, most research has focused on the spatial distribution of weeds within conventional input systems where weed communities tend to have patchy distributions (Dieleman et al., 1999; Faechner et al., 2002; Hughes, 1990; Mortensen et al., 1995; Rew and Cousens, 2001; Wiles et al., 1992) that are most likely due to differential mortality rates.
Despite this focus on conventional input cropping systems, there are still knowledge gaps relating to weed spatial patterns in conventional fields, especially in relation to species richness and density within weed patches. Furthermore, the effects of weed diversity on yield loss have not been adequately investigated. Cardina et al (1997) suggested that within a patch, weed intraspecific competition reduces the competition between the weeds and the crop. However, little is known about the relative importance of weed density and interspecific competition among weeds in relation to yield loss. It follows that if weed aggregation is going to be considered as a determinant of weed impact on crop yield, the characteristics (composition, density and diversity) of the aggregations themselves should also be considered.
Organic cropping systems, on the other hand, remain relatively unexamined with respect to weed spatial patterns. Casual field observations and anecdotal evidence suggest that weeds are not as patchy in organic systems as they are in conventional ones. Thus, it is reasonable to think that there might be spatial aspects of weed community structure within an organic cropping system that would influence crop-weed interactions differently than in conventional input systems. In addition, knowledge of spatial patterns in the weed communities of organic systems might allow for the implementation of site specific management practices.
Various methods have been used to map weed populations in farm fields. Discrete sampling is the most commonly used method, and involves collecting data from quadrats distributed on a grid (Rew et al., 2001). The data are then interpolated with techniques such as kriging to generate maps of weed distribution (Rew et al., 2001). Seedling counts from 0.25-m2 quadrats on a 10 m x 10 m grid have been shown to yield accurate maps of weed spatial distributions when kriging is applied (Heisel et al., 1996). However, determination of the proper grid size prior to sampling is essential for this type of technique, particularly when weeds are distributed in patches within a field, as larger grid sizes may lead to a decreased likelihood of recording any given patch of weeds (Rew and Cousens, 2001). In addition, grids are sometimes difficult to establish and the number of data points necessary to yield accurate maps is often very high (Rew and Cousens, 2001).
Continuous sampling is another option to assess weed spatial distribution (Rew and Cousens, 2001). With continuous sampling, the presence or absence of weeds is noted by an observer within the field while traveling in a continuous fashion. This data can then be analyzed using indices of aggregation to discern the general patterns of weed distribution. Quadrat variance techniques (Hill, 1973) can also be applied to continuous data, and can provide more insight into the spatial scale of patterns of vegetation. These types of analyses would be logical starting points in situations where the pattern of weed cover is unknown, and it is unnecessary to record discrete spatial patterns within the study area. In addition, these analyses would serve as some indication of whether the more intensive and detailed data collection needed to produce discrete spatial maps of weed cover would be useful.
Interference is an important factor that affects individual plants, plant populations, and plant communities (Harper, 1977). The effects of weed-crop competition in agricultural settings have been thoroughly assessed in some respects. In a model based approach using the inverse hyperbolic equation (Cousens, 1985), Garrett and Dixon (1998) determined that increasing the competition resulted in a diminishing yield response. Many field studies have found that crop yield decreases with increased weed density (Moechnig et al., 2003; O’Donovan and Sharma, 1983; Tamado et al., 2002; Whish et al., 2002) and that weed biomass decreased with increased crop density (Weiner et al., 2001). Weiner et al (2001) also found that weed density decreased as a result of planting crops in a grid pattern as opposed to a row pattern. However, the relationship between weed diversity and crop yield is unclear. Although Clements et al (1994) questioned if a more diverse weed community, at a constant density, would have a decreased effect on crop yield as compared to a less diverse weed community, no empirical study has yet evaluated this issue. There have been recent studies that suggest that weed diversity has had either negative (Davis et al., 2005) or positive (Suarez et al., 2001) effects on crop yield. Both of these studies were observational in nature, however, and did not control for weed density. Therefore, the yield effects observed could have been due to other factors besides diversity. In one study, Murphy et al (2006) demonstrated that crop yield was not affected by management system, even in light of increased weed diversity in some systems. This suggests that weed species diversity may have no effect on yield (Murphy et al., 2006). The link between weed diversity and crop yield has yet to be experimentally tested.
To study the effects of diversity on crop yield, it would be necessary to manipulate weed diversity and examine the effects of this manipulation on target crop plants as well as on the weeds themselves. The effects of neighboring plants on the growth of an individual plant are commonly studied using a running radius neighborhood competition approach. In this approach, a target plant is centered within a circular area of fixed radius, and all other plants within that area are considered neighbor plants (Silander and Pacala, 1985). Although neighborhood size is sometimes set arbitrarily, Silander and Pacala (1985) found that the optimum neighborhood size for relating neighbor plants to target plant performance was quite small (5cm radius). This lends support to the idea that plants only compete strongly with their nearest neighbors (Crawley, 1997a).
The effects of neighbor plants on target plants can be quantified in several ways. Using linear models, Hickman (1979) was able to account for 48 to 73% of the variation in dry weight biomass of Polygonum using the mean distance to four neighbors. Weiner (1982) was able to account for over 80% of the variation in individual seed set using a model which incorporated both distance to neighbors and their competitive effects. The number of independent factors that could be used in these models is quite expansive, but it is usually limited by logistical considerations. One potential problem with these types of analyses is that they measure absolute differences in biomass, seed production, or yield, and generally do not account for initial size inequality due to differences in physiology between species. One measure that accounts for the efficiency of biomass accumulation of a plant given the size of the plant is the relative growth rate, or RGR (Radosevich et al., 1997). The RGR also serves as a good approximation of competitive ability (Hegazy et al., 2005; Holt and Orcutt, 1991; Wang et al., 2006).
Measurements of relative growth rate can be achieved by accounting for plant biomass at the time of planting and then measuring plant biomass at harvest. Relative growth rate can also be measured at many points throughout the growing season by measuring an attribute of the plant which can be correlated with biomass. Bussler et al (1995) found that plant volume was very closely correlated with plant biomass. Thus, by measuring plant volume at several points throughout the growing season, plant biomass could be estimated without actually harvesting the plant. In this way, relative growth rate of a target plant or species could be measured over a growing season and between points within the growing season.
Project objectives:div style="margin-left:1em;">
Objective 1: Assess the spatial and temporal patterns of weed density and weed species richness in conventional input and organically managed spring wheat systems.
Objective 2: Quantify the effects of weed species richness and weed density on the growth and yield of spring wheat.