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Figure 3.5 Data sheet used by farmers to record weeds in annual crops in Nicaragua. Farmers observe both total cover and the presence of major types of weeds by phenological stage in 50 circular quadrats. This example was taken in a maize field 25 days after planting and before the first weeding.

Figure 3.5 Data sheet used by farmers to record weeds in annual crops in Nicaragua. Farmers observe both total cover and the presence of major types of weeds by phenological stage in 50 circular quadrats. This example was taken in a maize field 25 days after planting and before the first weeding.

Berti et al., 1992; Forcella, 1993; Gold, Bay & Wilkerson, 1996; Johnson et al., 1996). The primary objective is the one-time determination of whether the mean weed population density in a field is below or above a threshold that triggers application of a post-emergent herbicide.

In the context of farmer decision-making and a weed working group routine, additional reasons can be identified for documenting weed abundance, weed floristic composition, and weed patterns at the field and landscape levels. These include analysis of the timeliness of practices in farmer fields and researcher experiments, the evaluation of field-scale trials, and the comparison of weed dynamics among experiments and fields, and across years. For example, the format in Figure 3.5 was designed for use with smallholder maize and bean producers in Nicaragua. In a 15-30 minute walk through their fields (0.5-2.0 ha), farmers determine total weed cover and the presence and reproductive status of different weeds in 50 circular quadrats 25-30 cm in diameter. In a later group discussion, farmers compare problem weeds in different fields, total weed cover and crop stage, variability within each field, and the likelihood that the floristic composition will change based on current weed control practices. This method does not generate a spatial map, but it does provide information to analyze decisions on field-wide weed control and particular practices directed at specific weeds or patches.

The development of simple methods for on-farm use that combine accuracy and time efficiency is a major practical challenge to weed ecologists and weed extension specialists. Finding workable sampling and data recording methods for use in group discussion and for comparisons in time and space is one of the major initial areas of collaboration among farmers, exten-sionists, and scientists when developing new programs in participatory learning for action. A few basic guidelines are available from previous studies. A larger number of small units offers more precision than a smaller number of large units (Lemieux, Cloutier & Leroux, 1992). Greater sampling intensity is needed for accurate assessments of species that are less common (Marshall, 1988). Spatial distributions of weeds cannot be estimated with arithmetic interpolation from quadrat counts (Marshall, 1988). Transects can be used efficiently for sampling cover in large land areas (Morrison et al., 1993). Which is the best method? When should farmers sample? How frequently should they sample? Answers to these questions depend on the producers' interest, the types of weeds, the type of crop, field size, and specific concerns of the group. Midwestern USA maize and soybean growers and Central American maize and bean farmers grow similar crops, but would have very different discussions about weed variability and uncertainty, and would propose different observation methods.

Equipped with shared methods for weed measurement, farmers, exten-sionists, and researchers can develop site-specific and group-specific learning approaches as illustrated in the case studies of this chapter. These may be derived from individual or group initiatives, and vary in their degrees of collaboration as shown in Figure 3.6. The farmer group context keeps both scientist and farmer activities focused on farmer management of variability and uncertainty for fewer weeds and higher yields.

Farmers, extensionists, and scientists each have different potential rewards from participatory learning for action. Table 3.3 indicates how scientists who are worried about funding and publications, extension staff needing to cover their district with limited budget and time, and farmers who are concerned with crop prices and too much or too little rain might benefit from a working group routine based on participatory learning for action. Each plays an ample role in the advance of weed management; each has expectations to meet and procedures to follow in their own knowledge communities; and each has opportunities for creative working relationships with other sectors.

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