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Analyzing the impact of food price increases: assumptions about marketing margins can be crucial








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    Book (stand-alone)
    Border and related measures in the context of adaptation and mitigation to climate change
    The State of Agricultural Commodity Markets (SOCO): Background paper
    2018
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    Although international trade is not specifically mentioned in the Paris Climate Agreement, trade can play a facilitating role in achieving the mitigation and adaptation objectives of signatories to the Agreement. Trade policies can also undermine those objectives. The focus of this paper is on examining how the facilitating role of trade can be achieved. One of the challenges created by the ‘bottom-up’ approach of self-declared national mitigation targets adopted in the Agreement is that if the economic costs of greenhouse gas (GHG) emissions are internalized in production and consumption, the implicit price of carbon will differ across countries. This creates the potential for trade distortions. Domestic mitigation policies in importers will almost inevitably result in some carbon leakage, i.e. offsets to reductions in domestic emissions through additional emissions generated in supplying imports. But an important distinction needs to be made between carbon reallocation and carbon misallocation resulting from changes in trade volumes. In the reallocation case, trade leads to a shift in production to lower-emitting producers thereby contributing to global mitigation. In the misallocation case, the opposite occurs. This paper analyses how various border measures, including border tax adjustments (BTAs) might be used to reduce potential carbon misallocation. The conclusion is that technical and legal constraints on the effective application of border measures for food and agricultural products to prevent carbon misallocation are extremely challenging and their use could open the door to protectionism. The use of carbon standards and labelling offers an alternative approach to reducing misallocation and promoting reallocation. It poses fewer technical difficulties and reduces the potential for legal challenges. An added advantage of labelling is that it can help to promote changes in consumption that will be needed to reduce the carbon footprint of food and agriculture. The use of the approach could be facilitated through the adoption of international standards for carbon measurement and labelling, such as those being developed through the International Organization for Standardization (ISO). Labelling is not a panacea and may have limited effectiveness when consumers base their consumption decisions primarily on the basis of price. For this reason, the use of domestic policy measures that increase carbon efficiency in agriculture (reduce emissions per unit of output) and limit changes in land use that contribute to emissions will also be important for achieving mitigation aims under the Paris Agreement. An increasing number of regional trade agreements (RTAs) have incorporated environmental provisions, with the most common types of provisions focusing on environmental cooperation. Recent agreements recognise the importance of mutually supportive trade and environmental policies, and national commitments to multinational environmental agreements. RTAs could play a supporting role to the Paris Climate Agreement, by fostering international cooperation on climate mitigation measures in the context of freer trade.
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    Journal, magazine, bulletin
    Dairy Market Review - Overview of global dairy market developments in 2018
    mrt/19
    2019
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    Global milk output in 2018 is estimated at 842 million tonnes, an increase of 2.2 percent from 2017, driven by production expansions in India, Turkey, the EU, Pakistan, the United States and Argentina, but partially offset by declines in China and Ukraine, among few others. This increase has come about as a result of higher dairy herd numbers along with improvements to milk collection processes (India and Pakistan), efficiency improvements in integrated dairy production systems (Turkey), increased yield per cow (the EU and the United States) and enhanced utilization of idle capacity and higher demand from the processing sector and imports (Argentina). Milk output declines largely stemmed from industrial restructuring processes and downscaling of small-scale farms (China) and reduced producer margins and farm gate prices (Ukraine). Across the regions, Asia registered the highest milk output expansion by volume in 2018, followed Europe, North America. Milk output expanded in all other regions too, but by smaller volumes. World exports of dairy products expanded to 75 million tonnes (in milk equivalents), an increase of 2.1 million tonnes, or 2.9 percent from 2017, principally coming from the United States and Argentina, but also India, Uruguay, and Mexico. By contrast, exports declined in a number of countries, in particular in the Islamic Republic of Iran. Across the main dairy products, in 2018, SMP registered the highest export expansion (+8.6 percent), followed by butter (+7.5 percent), WMP (+1.7 percent) and cheese (+0.8 percent). As for milk powders, consisting of SMP and WMP, export availabilities were abundant from almost all major international suppliers. Large stocks of SMP, held by the EU, the United States and India, also contributed to elevate global supply availabilities. EU SMP stocks, given their age, were mostly considered less suitable for human consumption. In addition to immediate human consumption in the form of milk, powders were also in high demand from food processors and manufacturers, boosting import demand from some countries such as Mexico. Although butter exports for the whole year expanded, supplies were relatively limited in the first six months. Global supplies rose only when supplies from Oceania began entering the global markets, starting from about July, when its milk production season was in full swing. Butter import demand nevertheless was robust, especially from Asia, as urbanization, rising income and changing food habits made butter demand less price sensitive. Cheese exports expanded at a slower pace in 2018, compared to that of 2017, reflecting import cutbacks of many importers, including Australia and the United States. A robust market, however, existed for high value cheese products, boosted by rising consumer demand for specialized cheese varieties, also with geographic labelling. International dairy prices in 2018, measured by the FAO Dairy Price Index, declined by 4.6 percent compared to that of 2017, reflecting declines in prices of all dairy products represented in the Index, with the highest fall registered for SMP (-5.6 percent), followed by cheese (-5.2 percent), butter (- 4.4 percent) and WMP (-2.9 percent). The global supply-demand balances of each commodity, induced by factors discussed above, are compatible with these price movements. An additional factor that is noteworthy of mentioning on international dairy prices was the significant differentials that existed between the EU and Oceania on butter, WMP and SMP prices. Prices for butter and WMP in the EU hovered at higher levels than for Oceania, and that prices of SMP from Oceania were higher than those from the EU. Market segmentation, associated consumer preferences, reflecting geographical proximity to markets, was thought to be behind the observed price differentials across the two regions.
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    WCPFC: Evaluation of tag mixing assumptions for skipjack, yellowfin and bigeye tuna stock assessments in the western Pacific and Indian Oceans. 2013. WCPFC-SC9-2013/SA-IP-11 2013
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    ng (or mark-recapture) studies generally require the assumption that tagged and untagged individuals (of a particular demographic group) are equally likely to be captured. For tu, this usually translates into the assumption that tagged fish released from a relatively small region (selected for logistical reasons) mix rapidly over a much broader region of interest. In this paper, we apply the Comparison of Paired Recovery Distribution (CPRD) alysis to skipjack (Katsuwonus pelamis), yellowfin (Thu nnus albacares) and bigeye tu (Thunnus obesus) from large-scale tagging programmes in the Western Pacific and Indian Oceans. The CPRD alysis uses chi-square and nearest neighbour permutation statistical tests to calculate the probability that two tag recovery distributions from different release events are drawn from the same spatial distribution. The release events were separated in space and/or time and recovered in the same time window. If there is evidence that the two recovery distributions differ, it follows that tags from the two release events are not fully mixed with each other, such that at least one would also not be mixed with the untagged population either. Recovery events were defined with spatial boundaries corresponding to the most recent stock assessments, and alyses were restricted to release areas that were entirely within the recovery region. In the WCPO, there was strong evidence of incomplete mixing for 5 quarters following release for skipjack and yellowfin tus a nd 1 quarter for bigeye tu. For all 3 species, the observed periods of incomplete mixing is clearly a minimum, as there were insufficient observations to make inferences with respect to longer periods at liberty. In the Indian Ocean, there is strong evidence for incomplete mixing for skipjack for 3 quarters following release, 2 quarters for yellowfin and 1 quarter for bigeye. In contrast to the WCPO, useful numbers of CPRD events of longer duration were identified but did not show consistent com pelling evidence of incomplete mixing. The difference between the Pacific and Indian Ocean results may reflect genuine characteristics of mixing rates (e.g. tu in archipelagic waters seem to migrate shorter distances on average that tu in oceanic waters). However, the failure to detect incomplete mixing in the Indian Ocean may also reflect methodological limitations arising from the opportunistic ture of tag observations (e.g. the spatial distribution of Indian Ocean tag releases and recoveries was more restricted than in the WCPO). We expect that the mixing problem is serious enough to potentially introduce large biases to at least some of these stock assessments, but the magnitude of the biases may not be easy to quantify.

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