Impact of integrated pest management in rice and maize in the Greater Mekong Subregion
Bibliographic record
Abstract
Impact of integrated pest management in rice and maize in the Greater Mekong Subregion training farmers in IPM practices.At the end of 2017, CABI conducted an extensive assessment of the sustainability of TRFs and the impact of the interventions on IPM adoption in the target areas.The study found that the projects had led to significant reductions in chemical pesticide use among target rice farmers, as a result of the adoption of alternative IPM practices.The assessment also revealed the integral role of government policy in promoting the sustainable adoption of IPM in the Greater Mekong Subregion (GMS). Key highlights• At the end of the two projects in mid-2016, 20 TRFs were still producing Trichogramma eggcards; by the end of 2017 this number had dropped to 11.• The key reason for the termination of TRF production was a shortage of funds.After the projects came to an end, TRFs were reliant on government funding as none had yet transitioned to commercial production.• Farmers using egg-cards significantly decreased the amount spent on pesticide by appx 37% and almost halved the numbers of times crops were sprayed.• During the study, 45% to 100% of maize farmers reported an increase in yields whilst applying Trichogramma egg-cards.• Farmers expressed willingness to pay for Trichogramma egg-cards, but not at commercially sustainable prices.Government subsidies therefore appear to be necessary to support sustainable production of Trichogramma.• It is important to consider the economic and ecological context when selecting a target area to introduce biocontrol agents.Commercial markets for the widespread adoption and application of biocontrol agents do not exist in many GMS areas. ContextRice and maize are the two most important crops in Southwestern China, Laos and Myanmar, which make up part of the GMS.Rice is not only the primary source of food in the region, but also provides work and income for 80% of the population (Johnston et al., 2010).Similarly, maize is a staple crop for both human consumption and animal feed, and is produced by around 19 million farmers in the above mentioned countries.Despite significant improvements in rice production in the GMS over the past 15 years, irrigated rice yields have remained low, averaging 3-4 t/ha, in Laos and Myanmar (Heinrichs and Muniappan, 2017).One of the key reasons for these relatively low yields is the impact of pests, diseases and weeds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".