Dataset Information

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network.

ABSTRACT: The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the potential of Bayesian Belief Networks for describing the impacts of specific activities on climate change resilience, we developed a simple model that incorporates climate projections, local environmental data, information from peer-reviewed literature and expert opinion to account for the adaptation benefits derived from Climate-Smart Agriculture activities in Malawi. This novel approach allows assessment of vulnerability to climate change under different land use activities and can be used to identify appropriate adaptation strategies and to quantify biophysical adaptation benefits from activities that are implemented. We suggest that multiple-indicator Bayesian Belief Network approaches can provide insights into adaptation planning for a wide range of applications and, if further explored, could be part of a set of important catalysts for the expansion of adaptation finance.

SUBMITTER: de Nijs PJ

PROVIDER: S-EPMC4202202 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network.

de Nijs Patrick J PJ Berry Nicholas J NJ Wells Geoff J GJ Reay Dave S DS

Scientific reports 20141020

The need for smallholder farmers to adapt their practices to a changing climate is well recognised, particularly in Africa. The cost of adapting to climate change in Africa is estimated to be $20 to $30 billion per year, but the total amount pledged to finance adaptation falls significantly short of this requirement. The difficulty of assessing and monitoring when adaptation is achieved is one of the key barriers to the disbursement of performance-based adaptation finance. To demonstrate the pot ...[more]

PMID: 25327826

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Project description:Bangladesh's Haor regions are famous for their natural resources and are unable to escape climate vulnerability. Triggered by climate vulnerabilities farmers are heading towards climate-resilient approaches. Hence, research was done in the haor area of Sunamganj district to analyze the status of adopted Climate-Smart Agriculture (CSA) techniques in Chhatak, Sunamganj, and Jagannathpur which are prone to severe flooding and climate conditions. Around 450 farmers were randomly selected and CSA adopters were contacted. A structured questionnaire was prepared with open-ended and closed-ended questions. The final questionnaire contained demographic questions and a list of adopted cropland and homestead CSA practices, and the survey proceeded with 115 finalized CSA adopters. MS Excel and SPSS were used to analyze the data. The data were expressed using frequency, percent, mean, and standard deviation. A t-test, analysis of variance, multiple linear regression, Pearson correlation, boxplot, and normal P-P plots were employed to test data normality. The analysis revealed that 30 CSA practices were identified to be practiced in cropland where major preferences were found for appropriate seed storage (100%), USG application (100%), IPM (98%), and good quality seed (95%) in cropland, whereas agroforestry (71%), organic fertilizer application (63%), perching (63%) and IPM (59%) were major CSA practices among the 18 identified practices in homesteads. The adoption level of CSA practices was found in the score category of 11-23 for cropland (90%) and up to 10 for homestead (68%). The results showed that the adoption status of CSA practices was inefficient for quick flood occurrence. CSA practices are not applied enough in haor areas' homesteads due to lack of knowledge, information access, and technical and financial resources. Thus, CSA should be implemented which necessitates working on barriers restricting CSA adoption through strengthening the infrastructure of technologies, supportive policies, and institutional framework.

Project description:The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties. The few published qAOP models so far are typically based on data-demanding systems biology models. Here, we propose a less data-demanding approach for quantification of AOPs and AOP networks, based on regression modeling and Bayesian networks (BNs). We demonstrate this approach with the proposed AOP #245, "Uncoupling of photophosphorylation leading to reduced ATP production associated growth inhibition," using a small experimental data set from exposure of Lemna minor to the pesticide 3,5-dichlorophenol. The AOP-BN reflects the network structure of AOP #245 containing 2 molecular initiating events (MIEs), 3 key events (KEs), and 1 adverse outcome (AO). First, for each dose-response and response-response (KE) relationship, we quantify the causal relationship by Bayesian regression modeling. The regression models correspond to dose-response functions commonly applied in ecotoxicology. Secondly, we apply the fitted regression models with associated uncertainty to simulate 10 000 response values along the predictor gradient. Thirdly, we use the simulated values to parameterize the conditional probability tables of the BN model. The quantified AOP-BN model can be run in several directions: 1) prognostic inference, run forward from the stressor node to predict the AO level; 2) diagnostic inference, run backward from the AO node; and 3) omnidirectionally, run from the intermediate MIEs and/or KEs. Internal validation shows that the AOP-BN can obtain a high accuracy rate, when run is from intermediate nodes and when a low resolution is acceptable for the AO. Although the performance of this AOP-BN is limited by the small data set, our study demonstrates a proof-of-concept: the combined use of Bayesian regression modeling and Bayesian network modeling for quantifying AOPs. Integr Environ Assess Manag 2021;17:147-164. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Dataset Information

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network.

Publications

Quantification of biophysical adaptation benefits from Climate-Smart Agriculture using a Bayesian Belief Network.

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