Suchergebnisse

AbstractLegislation has been proposed that would change the European Union (EU) agricultural supply chain, including targeted reductions in the use of agricultural inputs. Our results indicate that reducing those inputs (fertilizer, pesticides, land, and antimicrobials) could lead to a reduction in the EU agricultural production by 12%, less competitiveness in export markets, and negative impacts to consumer budgets and societal welfare. If the EU were to implement technological improvements, the impacts would be lessened; however, the necessary improvement in technology will require additional investment in research and development. Based on historical trends, these improvements are projected to take up to 27 years.

AbstractMirror clauses have recently been discussed as a way of setting a level playing field for EU farmers in terms of the proposed European Green Deal (EGD) and the proposed reduction in usage of agricultural inputs. This study builds a Nash equilibrium model to determine if regions would implement these reductions to maintain open trade with the European Union. We find that more regions joining the EGD dampen the negative market impacts to the European Union, but we also find that major agricultural producers do not join the European Union in implementing the EGD.

Global agricultural trade, which increased at the end of 2020, has been described as "resilient" to the impacts of the COVID-19 coronavirus pandemic; however, the size and channels of its quantitative impacts are not clear. Using a reduced-form, gravity-based econometric model for monthly trade, we estimate the effects of COVID-19 incidence rates, policy restrictions imposed by governments to curb the outbreak, and the de facto reduction in human mobility/lockdown effect on global agricultural trade through the end of 2020. We find that while agricultural trade remained quite stable through the pandemic, the sector as a whole did not go unscathed. First, we estimate that COVID-19 reduced agricultural trade by the approximate range of 5 to 10 percent at the aggregate sector level; a quantified impact two to three times smaller in magnitude than our estimated impact on trade occurring in the non-agricultural sector. Second, we find sharp differences across individual commodities. In particular, we find that non-food items (hides and skins, ethanol, cotton, and other commodities), meat products including seafood, and higher value agri-food products were most severely impacted by the pandemic; however, the COVID-19 trade effect for the majority of food and bulk agricultural commodity sectors were found to be insignificant, or in a few cases, positive. Finally, we also examine the effects across low vs high income countries, the changing dynamics of the pandemic's effect on trade flows, and the effects along the extensive product margins of trade. ; Published version ; Public domain authored by a U.S. government employee

AbstractFocusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

AbstractPolicies that restrict the use of agricultural inputs have been shown to reduce output, farmers' incomes, and increase food prices, which could ultimately lead to more food insecurity. In this paper, we consider the EU Farm to Fork Strategy's proposed reductions of agricultural inputs on food security in 77 low‐ and middle‐income countries under two implementation scenarios: EU‐only and Global. Our findings indicate that compared with the status quo, each scenario results in a net increase in food insecurity, which ranges from 30 million (EU‐only) to 171 million (Global) by 2030.

Using economic models and empirical analysis, this volume examines a wide range of agricultural and biofuel policy issues and their effects on American agricultural and related agrarian insurance markets. Beginning with a look at the distribution of funds by insurance programs—created to support farmers but often benefiting crop processors instead—the book then examines the demand for biofuel and the effects of biofuel policies on agricultural price uncertainty. Also discussed are genetically engineered crops, which are assuming an increasingly important role in arbitrating tensions between energy production, environmental protection, and the global food supply. Other contributions discuss the major effects of genetic engineering on worldwide food markets. By addressing some of the most challenging topics at the intersection of agriculture and biotechnology, this volume informs crucial debates