Factors affecting adoption of improved management practices in the pastoral industry in Great Barrier Reef catchments

Rolfe, John; Gregg, Daniel

doi:10.1016/j.jenvman.2015.03.014

Cited by 33 publications

(24 citation statements)

References 20 publications

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“…There is a vast literature on the role of financial incentives in motivating BMP adoption, and its findings are consistent with the economic rational assumption that financial incentives can encourage BMP adoption [57,63,81,99,105]. More specifically, government subsidies [88,92,119] and credits or loans [70] were found to be positively correlated with adoption of BMPs.…”

Section: Financial Incentivessupporting

confidence: 60%

“…Farmers' risk aversion and environmental attitudes are receiving greater focus compared to a decade ago, but there is still room to better understand farmers' perception and attitudes in their conservation behavior [97][98][99][100].…”

Section: Findings: Factors Influencing Bmp Adoptionmentioning

confidence: 99%

“…For example, D'Emden et al [65] ascertained that the availability and use of technical information are important in the adoption of an erosion-reducing cropping system in southern Australia. However, smaller and non-traditional farmers might not have access to adequate conservation information and outreach and may not get the tools or training necessary to adopt better practices [98,99].…”

Section: Information and Awareness Of Bmpsmentioning

confidence: 99%

“…Agricultural producers' risk and time preferences are also factors in BMP adoption [9,97,99,112,113]. After capital costs, the risk of a loss of crop yield constituted the second most significant barrier to BMP adoption identified by California specialty crop growers [94].…”

Section: Risk and Time Preferences And Uncertaintymentioning

confidence: 99%

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Factors Influencing Farmers’ Adoption of Best Management Practices: A Review and Synthesis

2018

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Best management practices (BMPs) for reducing agricultural non-point source pollution are widely available. However, agriculture remains a major global contributor to degradation of waters because farmers often do not adopt BMPs. To improve water quality, it is necessary to understand the factors that influence BMP adoption by farmers. We review the findings of BMP adoption studies from both developed and developing countries, published after (or otherwise not included in) two major literature reviews from 2007 and 2008. We summarize the study locations, scales, and BMPs studied; the analytical methods used; the factors evaluated; and the directionality of each factor's influence on BMP adoption. We then present a conceptual framework for BMP adoption decisions that emphasizes the importance of scale, the tailoring or targeting of information and incentives, and the importance of expected farm profits. We suggest that future research directions should focus on study scale, on measuring and modeling of adoption as a continuous process, and on incorporation of social norms and uncertainty into decision-making. More research is needed on uses of social media and market recognition approaches (such as certificate schemes and consumer labeling) to influence BMP adoption.

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Section: Financial Incentivessupporting

confidence: 60%

Section: Findings: Factors Influencing Bmp Adoptionmentioning

confidence: 99%

Section: Information and Awareness Of Bmpsmentioning

confidence: 99%

Section: Risk and Time Preferences And Uncertaintymentioning

confidence: 99%

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Factors Influencing Farmers’ Adoption of Best Management Practices: A Review and Synthesis

2018

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“…2, yellow circles) samples 13 sites in inshore regions and in the GBR lagoon, and samples nutrients and Chl a extractions three times a year (Thompson et al, 2011;Rolfe and Gregg, 2015); no MMP bottle sampling occurred during the simulation period. The Australian Institute of Marine Sciences (AIMS) deployed moorings at the GBRMPA MMP sites from 2009 to 2014 and included Sea-Bird water quality monitors (WQMs) that measure chlorophyll fluorescence and turbidity, which are used for assessment purposes in this study.…”

Section: In Situ Observationsmentioning

confidence: 99%

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

et al. 2016

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Abstract. Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO 3 , NH 4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially derived TSS and CDOM or the lack of a calibrated regional IOP algorithm.

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Best management practices for agricultural nonpoint source pollution: Policy interventions and way forward

Jain

Singh

2019

World Water Policy

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Best management practices (BMPs) are the tools which are adopted to run any activity while inflicting least negative impacts on the surrounding environment. Since, water pollution is one of the most important issues in today's world; it is indispensable to discuss the activities which are deteriorating its quality. Although, a number of treatment technologies and methods are in place for treating the point sources of pollution, there is a need to address nonpoint sources of pollution. There are many successful practices which are capable of curtailing the impacts of nonpoint source pollution, if adopted. In this paper an attempt has been made to evaluate the performance and trade‐offs of various management practices to address the issues of agricultural nonpoint source pollution. Nevertheless, there is a dearth of guiding principles which can pave a way for adoption of these practices. Therefore, an attempt has also been made to explore the aspects of policy formulations along with emphasizing the factors which can lead to adoption/ non‐adoption of such practices.

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Factors affecting adoption of improved management practices in the pastoral industry in Great Barrier Reef catchments

Cited by 33 publications

References 20 publications

Factors Influencing Farmers’ Adoption of Best Management Practices: A Review and Synthesis

Factors Influencing Farmers’ Adoption of Best Management Practices: A Review and Synthesis

Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

Best management practices for agricultural nonpoint source pollution: Policy interventions and way forward

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