- Vegetables: cabbages
- Crop Production: biological inoculants
- Farm Business Management: whole farm planning
- Pest Management: general pest management
- Production Systems: agroecosystems
From our studies in the laboratory and in the field, the egg parasitoid Trichogramma (Hymenoptera, Chalcidoidea) willingly parasitized diamondback moth (DBM) eggs on cabbage. The species of Trichogramma investigated were Trichogramma pretiosum (Riley), Trichogrammatoidea bactrae (Nagaraja) and Trichogramma minutum (Riley). All parasitized DBM eggs, even in a two-host scenario. The alternative host examined was the soybean looper (Pseudoplusia includens (Walker)), a common pest of vegetables on the south coast of Puerto Rico. Even though the soybean looper is considered a favorable host of Trichogramma, it was the DBM eggs that were preferentially parasitized in all three species. The greatest preference for DBM was shown by T. pretiosum, while T. bactrae, had the higher overall parasitism levels. Trichogramma minutum expressed the lowest levels of parasitism. The fieldwork showed that the two lepidopteran species oviposited their eggs in different locations on the cabbage plant. Their relative vertical positions on the cabbage plants varied depending on the size of the cabbage plants and on the particular experiment. Commercial T. pretiosum was used in the fieldwork and this insect conducted most of its parasitism in the lower reaches of the plants. Due to its foraging/parasitizing behavior, percent parasitism of the two host’s eggs depended on how low on the cabbage plants the bulk of the eggs were oviposited. Percent parasitism therefore was a combination of the inherent acceptability of the host eggs and the positioning of the host eggs relative to the parasitoid search. In our fieldwork there was generally higher parasitism of DBM eggs than soybean looper eggs. In those experiments where DBM eggs were oviposited close to the ground, parasitism levels were at their highest. Parasitism levels of soybean looper eggs, on the other hand were low, even when the eggs were found close to the ground. This is thought to be because of its lower acceptability as host to T. pretiosum.
A socioeconomic study was conducted in the mountainous central region of Puerto Rico; and a linear programming (LP) model was developed to study the farming system of this region. A series of interviews and other data-gathering exercises gave detailed information on farm activities and the factors that influence the farming system. The LP model combined this information and gave a fair representation of the farming system. Most of the farm activity occurs during the Christmas period, while the summer months are normally a time of low activity. The government’s incentive scheme sustains many of the activities found; and leads to plantain being a dominant crop. Poor, unstable markets seem to be the biggest constraint to this system. Agrochemical use is limited in many of the crops, especially roots and tubers. Any introduced IPM technique would have to focus, primarily, on keeping labor costs down.
Insect Pests in Cabbage and the Failure to
Control them using Insecticides
About 15 species of insect pests of cabbage in Puerto Rico have been recorded (Agricultural Experimental Station, University of Puerto Rico, 1999). These include the diamondback moth, whitefly, leafminers, loopers and the imported and gulf white cabbageworms. The diamondback moth (Plutella xylostella) (Linnaeus) is by far the most important pest of cabbage on the island. Both the cabbage looper (Trichoplusia ni Hübner) and the soybean looper (Pseudoplusia includens Walker) can also be found in the field, but the latter is more common. In the University of Puerto Rico’s Research Station’s technological package on cabbage (1999), loopers are noted as the second most important pest of cabbage.
Both the diamondback moth (DBM) and the soybean looper (SL) are known to have developed resistance to a variety of insecticidal compounds that have been used in Puerto Rico and elsewhere (Armstrong 1990, Boethel et al. 1992, Leibee and Savage 1992, Shelton et al. 1993, Gianessi et al. 2002, Zhao et al. 2002). It is for this reason that the biological control of these insect pests has been pursued. Although most of the research has been directed at the better-known larval parasitoids—in particular Cotesia spp. (Hymenoptera: Braconidae) and Diadegma spp. (Hymenoptera: Ichneumonidae)—egg parasitoids also have the potential to significantly reduce populations of these pests. DBM and the loopers are thought to have few natural egg parasitoids (Harcourt 1962, Lundgren et al 2002) and so it has been postulated that effective control may be achieved by filling this niche (Oatman et al. 1968) with the Trichogramma egg parasitoids.
Trichogramma Egg Parasitoids and Host Selection
Trichogramma’s ability to recognize and use a variety of host species is considered to be an evolutionary adaptation to its multivoltine life cycle and limited capacity to control its dispersal in the field (Sachtleben 1929). Its polyphagous nature does not signify, however, that it is indiscriminate in its choice of host eggs. Trichogramma carefully determines the number and the sex of the eggs laid into chosen hosts. It does so by an assessment of host size, age, nutritional suitability and previous parasitism of the host egg amongst other things (Quednau 1855, Klomp and Teerink 1962, Nettles et al. 1983, De Jong and Pak 1984, Pak 1986, Pak and De Jong 1987, Bin et al. 1993, Schmidt 1994, Consoli et al. 1999). In terms of egg parasitism and host quality, the small size of DBM eggs (~0.02 mg—0.44 mm long 0.26 mm wide) may reduce their perceived suitability to ovipositing Trichogramma females. It has been shown that Trichogramma wasp body size, fecundity, and longevity are dependent on larval feeding (Charnov and Skinner 1985). Below a certain host egg volume, small adults emerge, as space and availability of sufficient nutrients limit larval development (Greenberg et al. 1998). In general, most Trichogramma species are reported to prefer intermediate to large host eggs, often 0.8–1.8 mm in diameter (Schmidt 1994). In addition to possible low parasitism levels due to size, how do DBM eggs compare as potential hosts when in the presence of other host species’ eggs? As mentioned before, Trichogramma parasitizes the eggs of other members of the cabbageworm complex, attesting to their suitability as hosts. Loopers, in fact, are used as factitious hosts in laboratory colonies of Trichogramma (Hohmann et al. 1988, Kazmer and Luck 1995) and in the literature there is note that Trichogramma pretiosum is considered to have become adapted to genera of the Noctuidae (Monje et al. 1999). The global importance and ubiquitous presence of DBM makes it the chosen target in most control programs and research efforts. It is therefore important to find species or strains of Trichogramma that effectively parasitize DBM eggs, even when in the presence of other host eggs. Soybean looper (Pseudoplusia includens) was used in these experiments as the alternate host to DBM. The research was conducted in Puerto Rico where the soybean looper (SL) is an important, widespread pest in vegetable crops and likely to be found in cabbage plantings, alongside the DBM. The SL egg is significantly larger than the DBM egg (0.6 mm in diameter and 0.4 mm in height) and, as with other noctuids, is considered a good host for Trichogramma pretiosum (Monje et al 1999). How would the presence of this alternate, and larger host affect parasitism levels of DBM eggs by T. pretiosum? A series of field and laboratory experiments were conducted to examine Trichogramma pretiosum’s parasitism of DBM in Puerto Rico and to see whether SL would be considered a more acceptable host. In addition to examining host preference, the series of field experiments conducted in 2001 and 2002 were designed to determine ovipositional patterns of the two host species (DBM and SL) and to look for parasitism patterns of the Trichogramma with respect to plant architecture. The laboratory experiments expanded on the basic host preference objectives and included protocols to examine the influence of prior ovipositional experience. In addition to this, two other Trichogramma species were also included in the basic host preference experiments. The two other species of Trichogramma (Trichogrammatoidea bactrae and Trichogramma minutum) were chosen, based on a literature search, which determined that both species were considered strong candidates in previous work.
Farming Systems Research
A farming system is defined by the FAO as “a population of individual farm systems that have broadly similar resource bases, enterprise patterns, household livelihoods and constraints, and for which similar development strategies and interventions would be appropriate” (F.A.O. 2003). Keating and McCown (2001) identify two key components of farming systems, namely the biophysical ‘Production System’ of crops, animals, soil and climate together with certain physical inputs and outputs and the ‘Management System’, made up of people, values, goals, knowledge, resources, monitoring opportunities and decision making. Their review of six types of farming systems analysis concludes that the challenges and opportunities lie at the interface between the ‘hard’, scientific approaches to the analysis of the biophysical system and ‘soft’ approaches to intervention in social management systems. They also conclude that the use of models in farmer decision support systems has been disappointing and a way has to be found of making models relevant to real world decision-making and management practices. This may not necessarily be achieved by only making the models more accurate or more comprehensive. Part of the scientific process is the deconstruction of problems so that individual elements can be identified, appraised, experimented on and understood. They are either left as they are, or altered in the hope of improvement. A weakness in the scientific process is that the deconstruction removes the elements from their natural position, and contextually, this can lead to misinterpretations and oversights. The place of an element in a particular system is as important as the intrinsic characteristics of the element itself. Importantly, the producers themselves see their environment as a system (Dixon et al. 2001) and evaluate new technologies by the way in which they interact with the other elements of their environment. Success is based on a perceived over-all betterment of the system. How can the totality of a system be understood? Farmer Participatory Research (FPR) is a good way of studying communities and understanding their characteristics. It relies on human interactions and observations and is built on synergistic collaborations of multi-disciplinary technical teams, community members and other stakeholders such as extension agents (Dlott et al. 1994, Biggs 1989, Chambers 1994, Cornwall and Jewkes 1995). Participatory research methods have become an integral part of Farming Systems Research (FSR), and they serve as an important means of dialogue between participants and stakeholders.
FSR is principally about technology generation, and its processes can be divided into four stages: descriptive (diagnostic), design, testing and extension (Norman 1980). Stakeholder feedback is crucial to all stages. The first stage is about understanding the livelihood system and generating research objectives based on identified problems or possibilities. The second stage determines how best the research objectives can be met by planning an effective and efficient set of research activities. The third stage is the execution of these activities. This stage is given validity by its inclusiveness, its relevance and its interactivity. The final stage disseminates results and implements new technologies. The purpose of the farming systems analysis conducted in the three municipalities of the central region of Puerto Rico (Barranquitas, Naranjito & Orocovis) was to form a better understanding of how the farms function and how IPM practices could be incorporated. Specifically, the objective was to see how cabbage might be grown on these farms using an IPM methodology for the control of diamondback moth that included the use of Trichogramma. The mountain farms in this region were never developed or supported in the same way as the larger farms of the coastal areas, and both the industrialization and agricultural intensification programs of the last century have had little positive impact. They still remain key, however, to the identity and livelihoods of the people who live in this region of Puerto Rico. It is for this reason that methods should be identified that could help characterize the farming systems present and help promote sustainable development within these areas. This research project attempted to use linear programming models as a means of characterizing and studying the farming system of the central region of Puerto Rico.
This is a form of modeling using an optimization matrix program that, for the purposes of FSR, looks to examine the interface of ‘hard’ scientific approaches and ‘soft’ approaches in social management. It does so by simulating and analyzing family farm livelihood systems by determining the optimal combination of farm and non-farm activities that is feasible, given a set of fixed constraints (Cabrera 1999). LP models are not as exact in their simulation of production functions as some crop models, and are not as sophisticated as some economic models, but they do represent a robust and fairly simple means of characterizing farming systems. They also provide a way of numerating assessments of how alternative activities achieve household objectives. To construct a linear programming (LP) model, certain information is needed. Hildebrand and Araújo (1997) state that the following is needed: 1) the farm and non-farm activities and options with their respective resource requirements and any constraints on their production; 2) the fixed resources and other maximum or minimum constraints that limit farm and family production; 3) cash costs and returns of each activity; and 4) a defined objective or objectives. With this information, LP models can be made to simulate the characteristics and activities found on farms that go beyond merely identifying the most productive strategies. The use of LP models in farm planning has its origins in the late 1950s, when whole farm planning was been developed. In 1958, Heady and Candler outlined the application of LP modeling to farm planning, and by 1963, its relevance to low-income agriculture had been demonstrated (Clayton 1963). Since then, it has been widely used to examine supply changes and policy shifts in agriculture (Hazell and Norton 1986). Its impact on improving livelihoods in developing countries, however, has never been great, in part due to the laborious data collecting process and to its lack of direct applicability (Collinson 2000). How then does LP modeling fit into FSR? Its primary use is in the first stage (description/diagnostic phase) and second stage (research-planning) of FSR, but can also be used as an extension tool in stage 4. Norton et al. (1999) describe an approach to participatory IPM research that is being implemented by the USAID-supported IPM Collaborative Research Support Program (IPM CSRP). One of the early activities in the IPM CSRP approach is the participatory appraisal stage that can take from between one to two weeks. It is at this stage; with disparate sets of community data or on-farm data collected, that linear program (LP) modeling could be most useful. All the information gathered by the various participants could be distilled into a matrix representing the enterprise activities (resource requirements and production functions), the farm’s constraints and resources (land, labor, capital, costs – on both spatial and temporal levels) and household objectives. Once a model has been validated, and it accurately reflects the farming systems under question, the designing of experiments (stage 2 of the FSR approach) can proceed. Alternatively, validated models can be used to assess already existing technologies to see if they would be worth implementing into the farming system under study. Used properly, LP modeling can be a very useful tool to FSR practitioners.
- Investigate the host preferences of Trichogramma, specifically the preferences between the soybean looper (Pseudoplusia includens) and diamondback moth (Plutella xylostella). This will be accomplished through field trials and greenhouse experiments.
Assess field releases of Trichogramma with respect to their impact on lepidopteran pest populations.
Examine the socioeconomic influences on cabbage growers with respect to their acceptance and use of biological control practices