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Is Bhutan destined for 100% organic? Assessing the economy-wide effects of a large-scale conversion policy.

ABSTRACT: Organic agriculture (OA) is considered a strategy to make agriculture more sustainable. Bhutan has embraced the ambitious goal of becoming the world's first 100% organic nation. By analysing recent on-farm data in Bhutan, we found organic crop yields on average to be 24% lower than conventional yields. Based on these yield gaps, we assess the effects of the 100% organic conversion policy by employing an economy-wide computable general equilibrium (CGE) model with detailed representation of Bhutan's agricultural sector incorporating agroecological zones, crop nutrients, and field operations. Despite a low dependency on agrochemicals from the onset of this initiative, we find a considerable reduction in Bhutan's GDP, substantial welfare losses, particularly for non-agricultural households, and adverse impacts on food security. The yield gap is the main driver for a strong decline in domestic agricultural production, which is largely compensated by increased food imports, resulting in a weakening of the country's cereal self-sufficiency. Current organic by default farming practices in Bhutan are still underdeveloped and do not apply the systems approach of organic farming as defined in the IFOAM organic farming standards. This is reflected in the strong decline of nitrogen (N) availability to crops in our simulation and bears potential for increased yields in OA. Improvement of soil-fertility practices, e.g., the adoption of N-fixing crops, improved animal husbandry systems with increased provision of animal manure and access to markets with price premium for organic products could help to lower the economic cost of the large-scale conversion.

SUBMITTER: Feuerbacher A

PROVIDER: S-EPMC5999226 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Is Bhutan destined for 100% organic? Assessing the economy-wide effects of a large-scale conversion policy.

Feuerbacher Arndt A Luckmann Jonas J Boysen Ole O Zikeli Sabine S Grethe Harald H

PloS one 20180613 6

Organic agriculture (OA) is considered a strategy to make agriculture more sustainable. Bhutan has embraced the ambitious goal of becoming the world's first 100% organic nation. By analysing recent on-farm data in Bhutan, we found organic crop yields on average to be 24% lower than conventional yields. Based on these yield gaps, we assess the effects of the 100% organic conversion policy by employing an economy-wide computable general equilibrium (CGE) model with detailed representation of Bhuta ...[more]

PMID: 29897989

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Project description:Under the current policy decision making paradigm we make or evaluate a policy decision by intervening different socio-economic parameters and analyzing the impact of those interventions. This process involves identifying the causal relation between interventions and outcomes. Matching method is one of the popular techniques to identify such causal relations. However, in one-to-one matching, when a treatment or control unit has multiple pair assignment options with similar match quality, different matching algorithms often assign different pairs. Since all the matching algorithms assign pairs without considering the outcomes, it is possible that with the same data and same hypothesis, different experimenters can reach different conclusions creating an uncertainty in policy decision making. This problem becomes more prominent in the case of large-scale observational studies as there are more pair assignment options. Recently, a robust approach has been proposed to tackle the uncertainty that uses an integer programming model to explore all possible assignments. Though the proposed integer programming model is very efficient in making robust causal inference, it is not scalable to big data observational studies. With the current approach, an observational study with 50,000 samples will generate hundreds of thousands binary variables. Solving such integer programming problem is computationally expensive and becomes even worse with the increase of sample size. In this work, we consider causal inference testing with binary outcomes and propose computationally efficient algorithms that are adaptable for large-scale observational studies. By leveraging the structure of the optimization model, we propose a robustness condition that further reduces the computational burden. We validate the efficiency of the proposed algorithms by testing the causal relation between the Medicare Hospital Readmission Reduction Program (HRRP) and non-index readmissions (i.e., readmission to a hospital that is different from the hospital that discharged the patient) from the State of California Patient Discharge Database from 2010 to 2014. Our result shows that HRRP has a causal relation with the increase in non-index readmissions. The proposed algorithms proved to be highly scalable in testing causal relations from large-scale observational studies.

Dataset Information

Is Bhutan destined for 100% organic? Assessing the economy-wide effects of a large-scale conversion policy.

Publications

Is Bhutan destined for 100% organic? Assessing the economy-wide effects of a large-scale conversion policy.

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