Effectivity and Cost Efficiency of a Tax on Nitrogen Fertilizer to Reduce GHG Emissions from Agriculture

Meyer‐Aurich, Andreas; Karatay, Yusuf Nadi; Nausėdienė, Aušra; Kirschke, Dieter

doi:10.3390/atmos11060607

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…Being risk-averse is associated with a lower level of total fertilization (−48.8 kgN/ha on average) with respect to the reference category (risk neutrality). This result is not in accordance with the main conclusion in the literature [9,10,[13][14][15]: risk aversion leads to an over-fertilization. Nevertheless, this result is interesting but difficult to analyze because it can be an indication that nitrogen fertilizers are perceived as being either risk-increasing or risk-decreasing (or neutral) by farmers, whereas the global levels of risk were perceived as being very low by farmers who self-insure with fertilizers.…”

Section: Discussioncontrasting

confidence: 90%

“…More recently, Meyer-Aurich and Karatay [13] concludes that, in most of their experimental sites, farmer's risk aversion do not seem to be a strong argument to apply less N fertilizer. The same conclusion is obtained in another article testing the effectivity and cost efficiency of a tax on nitrogen fertilizer [14]. Finally, two recent papers dealing with this problematic in China [15,16] show that the farmer's risk aversion is associated with a higher level of fertilization.…”

Section: Introductionsupporting

confidence: 70%

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Role of Farmers’ Risk and Ambiguity Preferences on Fertilization Decisions: An Experiment

Tevenart

Brunette

2021

Sustainability

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In the context of climate change, the agricultural sector offers a large number of mitigation possibilities through diverse practices, such as the reduction of pollutant inputs. However, most farmers do not adopt the mitigation practices recommended, including the reduction of nitrogen fertilization. At the same time, various uncertainties characterize agricultural production, so that the farmer’s risk and ambiguity preferences may be potential determinants to the adoption of mitigation practices. In this context, the objective of the article is to determine if the farmer’s risk and ambiguity preferences explain (or not) the fertilization decision. A questionnaire was submitted to French farmers to elicit risk and ambiguity preferences through lottery choices, and ask questions about fertilization. Two regressions were realized, the first to explain the total fertilization and the second to identify the determinants of the first fertilization application. The results reveal that respondents were mostly risk-averse and ambiguity-neutral. In addition, risk and ambiguity aversion impact fertilization practices through diverse drivers in opposite directions. Indeed, being risk-averse is associated with a lower level of total fertilization, whereas ambiguity aversion has a positive and significant impact on the level of fertilization at the first application. This last result highlights the need to reduce the uncertainty farmers face.

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Section: Discussioncontrasting

confidence: 90%

Section: Introductionsupporting

confidence: 70%

Role of Farmers’ Risk and Ambiguity Preferences on Fertilization Decisions: An Experiment

Tevenart

Brunette

2021

Sustainability

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“…This type of pollution is also influenced by the intensive use of nitrogen fertilizers [59,60] to increase yield [61]. Excessive use of nitrogen fertilizers in agriculture increases environmental pollution [62,63], which would also reduce GHG emissions.…”

Section: From Urea Applicationmentioning

confidence: 99%

Logarithmic Mean Divisia Index Decomposition Based on Kaya Identity of GHG Emissions from Agricultural Sector in Baltic States

2022

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Greenhouse gas (GHG) emissions from agriculture contribute to climate change. The consequences of unsustainable agricultural activity are polluted water, soil, air, and food. The agricultural sector has become one of the major contributors to global GHG emissions and is the world’s second largest emitter after the energy sector, which includes emissions from power generation and transport. Latvian and Lithuanian agriculture generates about one fifth of GHG emissions, while Estonia generates only about one tenth of the country’s GHG emissions. This paper investigates the GHG trends in agriculture from 1995 to 2019 and the driving forces of changes in GHG emissions from the agricultural sectors in the Baltic States (Lithuania, Latvia, and Estonia), which are helpful for formulating effective carbon reduction policies and strategies. The impact factors have on GHG emissions was analysed by using the Logarithmic Mean Divisia Index (LMDI) method based on Kaya identity. The aim of this study is to assess the dynamics of GHG emissions in agriculture and to identify the factors that have had the greatest impact on emissions. The analysis of the research data showed that in all three Baltic States GHG emissions from agriculture from 1995 to 2001–2002 decreased but later exceeded the level of 1995 (except for Lithuania). The analysis of the research data also revealed that the pollution caused by animal husbandry activities decreased. GHG intensity declined by 2–3% annually, but the structure of agriculture remained relatively stable. The decomposition of GHG emissions in agriculture showed very large temporary changes in the analysed factors and the agriculture of the Baltic States. GHG emissions are mainly increased by pollution due to the growing economy of the sector, and their decrease is mainly influenced by two factors—the decrease in the number of people employed in the agriculture sector and the decreasing intensity of GHGs in agriculture. The dependence of the result on the factors used for the decomposition analysis was investigated by the method of multivariate regression analysis. Regression analysis showed that the highest coefficient of determination (R2 = 0.93) was obtained for Estonian data and the lowest (R2 = 0.54) for Lithuanian data. In the case of Estonia, all factors were statistically significant; in the case of Latvia and Lithuania, one of the factors was statistically insignificant. The identified GHG emission factors allowed us to submit our insights for the reduction of emissions in the agriculture of the Baltic States.

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“…Three N prices were chosen: 0.50, 0.65 and 1.00 $ kg -1 . The 0.50 and 0.75 represents the current range in the cost of N fertilizer while the $1.00 kg -1 price represents an elevated price that might be established by government policy to reduce N fertilizer use to combat global warming (Meyer-Aurich et al 2020). Three grain prices were chosen: 0.44, 0.66 and 0.88 $ kg -1 .…”

Section: Change In Gross Returnmentioning

confidence: 99%

“…When evaluating an appropriate N rate to recommend for annual canarygrass production it is important to consider the sensitivity of the N rate recommendation to changes in the price of crop and N fertilizer. This becomes even more important if in the future N fertilizer prices are elevated to reduce excess N fertilization and green house gas production (Meyer-Aurich et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Response of annual canarygrass (Phalaris canariensis L.) to nitrogen fertilizer and fungicide applications

2022

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Annual canarygrass (Phalaris canariensis L.) is a specialty crop grown in Canada and the harvested grain is primarily used to feed wild and domesticated bird species. A field experiment was conducted at 5 locations in both 2012 and 2013 to study the response of annual canarygrass development and grain yield to the combined effects of fungicide (propiconazole + trifloxystrobin) and nitrogen (N) fertilizer and to determine the minimum number of site-years required to detect these effects. The experimental design was a split plot with fungicide application (none or fungicide) as the main plot and N application rate as the sub plot (10, 20, 30, 50, 70, 90 kg N ha-1). There was a linear increase of 14.5% in grain yield as the rate of N fertilizer increased. The fungicide application increased the grain yield 8.5% by increasing kernel density from 15,197 kernels m-2 to 16,288 kernels m-2. There was no interaction between the N rate and fungicide application. The application of a fungicide did not increase the responsiveness of annual canarygrass to N fertilizer. The lack of an interaction between N and fungicide application indicates that these two practices can be managed independently by annual canarygrass producers. To optimize grain yield producers should apply 50 kg N ha-1 and apply a fungicide to increase grain yield especially in regions where septoria leaf mottle is prevalent. The number of site years needed to consistently detect the impact of N and fungicide on the grain yield were 4 and 5 site-years, respectively.

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Effectivity and Cost Efficiency of a Tax on Nitrogen Fertilizer to Reduce GHG Emissions from Agriculture

Cited by 16 publications

References 24 publications

Role of Farmers’ Risk and Ambiguity Preferences on Fertilization Decisions: An Experiment

Role of Farmers’ Risk and Ambiguity Preferences on Fertilization Decisions: An Experiment

Logarithmic Mean Divisia Index Decomposition Based on Kaya Identity of GHG Emissions from Agricultural Sector in Baltic States

Response of annual canarygrass (Phalaris canariensis L.) to nitrogen fertilizer and fungicide applications

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