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1.
J Environ Manage ; 357: 120763, 2024 Apr.
Article in English | MEDLINE | ID: mdl-38581892

ABSTRACT

Agri-environmental programs (AEPs), which pay farmers to adopt conservation practices, are increasingly important environmental and agricultural policy tools used in both the US and the EU. Despite large budgetary shares allocated in a 20-year application window, several studies identify lower-than-expected environmental outcomes. In the US, reasons for low environmental outcomes include low participation rates, lack of program awareness, and poor targeting levels. Research has identified low levels of awareness and variation in participation across the US in the Environmental Quality Incentives Program (EQIP), the largest working lands program in the US. Using a survey of Western US farmers, this paper provides an analysis of awareness and participation levels in cover crop AEPs in the Western US. Second, this paper analyzes motivations and barriers to participation in cover crop AEPs through the Environmental Quality Incentives Program (EQIP) and the Conservation Stewardship Program (CSP). Third, the paper uses a survey experiment to examine different types of incentives. The results highlight that participation is low due to lack of awareness and policy barriers. Using a logistic regression, predictors of AEP participation include frequent contact with NRCS, having a succession plan, and a positive attitude toward governments' role in conservation programs. The survey experiment found that non-financial factors, such as more information on cover crops, is an effective policy incentive.


Subject(s)
Conservation of Natural Resources , Farmers , Humans , Conservation of Natural Resources/methods , Agriculture/methods , Motivation , Surveys and Questionnaires
2.
J Adolesc Health ; 51(2): 184-9, 2012 Aug.
Article in English | MEDLINE | ID: mdl-22824450

ABSTRACT

PURPOSE: Reconsent involves asking research participants to reaffirm their consent for study participation when there have been significant changes in the study's procedures, risks, or benefits. We described the reconsent process, identified the reconsent rate, and examined the comparability of youths enrolled via consent and reconsent in a national evaluation of adolescent reproductive health programs. METHODS: Evaluation participants from five abstinence education projects (N = 2,176) and nine projects serving pregnant or parenting adolescents (N = 878) provided either parent or youth consent or reconsent to participate in the national evaluation. Participants completed surveys that included demographic characteristics; sexual intentions, norms and behaviors; and pregnancy history. Multivariate logistic regression was used to examine associations between consent status, demographic characteristics, and risk indicators. RESULTS: The reconsent rates in the abstinence education and pregnant or parenting samples were 45% and 58%, respectively. Participant's age was positively associated with reconsent. Hispanic adolescents (and, for abstinence education, other racial/ethnic minorities) were underrepresented among youth with reconsent. Among abstinence education study participants, risk indicators were not associated with consent status. Among pregnant or parenting teens, those who had experienced repeat pregnancy were less likely than those who had experienced only one pregnancy to have been enrolled via reconsent. CONCLUSIONS: Reconsent can bolster sample size but may introduce bias by missing some racial/ethnic and age-groups. Among high-risk adolescents, reconsent may also yield a sample that differs from consented samples on risk characteristics, necessitating statistical adjustments when analyzing data.


Subject(s)
Informed Consent/statistics & numerical data , Reproductive Health Services/standards , Sex Education , Sexual Abstinence , Adolescent , Child , Data Collection , Female , Hispanic or Latino/statistics & numerical data , Humans , Informed Consent/standards , Male , Parenting , Pregnancy , Pregnancy in Adolescence , Program Evaluation/standards , Regression Analysis , Risk , United States
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