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Agricultural commodities used for feed and food production are frequently contaminated with mycotoxins, such as Aflatoxin B1 (AFB1). In Europe, both the government and companies have monitoring programs in place for the presence of AFB1. With limited resources and following risk-based monitoring as prescribed in EU Regulation 2017/625, these monitoring programs focus on batches with the highest probability of being contaminated. This study explored the use of machine learning algorithms (ML) to design risk-based monitoring programs for AFB1 in feed products, considering both monitoring cost and model performance. Historical monitoring data for the presence of AFB1 in feed products (2005–2018; 5605 records in total) were used. Four different ML algorithms, including Decision tree, Logistic regression, Support vector machine and Extreme gradient boosting (XGB), were applied and compared to predict the high-risk feed batches to be considered for further AFB1 sampling and analysis. The monitoring cost included the cost of: sampling and analysis, disease burden, storage, and of recalling and destroying contaminated feed batches. The ML algorithms were able to predict the high-risk batches, with an AUC, recall, and accuracy higher than 0.8, 0.6, and 0.9, respectively. The XGB algorithm outperformed the other three investigated ML. Its incorporation would result into up to 96% reduction in monitoring cost in 2016–2018, as compared to the official monitoring program. The proposed approach for designing risk based monitoring programs can support authorities and industries to reduce the monitoring cost for other food safety hazards as well. [ABSTRACT FROM AUTHOR]

Background: Economic impact assessment of invasive species requires integration of information on pest entry, establishment and spread, valuation of assets at risk and market consequences at large spatial scales. Here we develop such a framework and demonstrate its application to the pinewood nematode, Bursaphelenchus xylophilus, which threatens the European forestry industry. The effect of spatial resolution on the assessment result is analysed. Methodology/Principal Findings: Direct economic impacts resulting from wood loss are computed using partial budgeting at regional scale, while impacts on social welfare are computed by a partial equilibrium analysis of the round wood market at EU scale. Substantial impacts in terms of infested stock are expected in Portugal, Spain, Southern France, and North West Italy but not elsewhere in EU in the near future. The cumulative value of lost forestry stock over a period of 22 years (2008-2030), assuming no regulatory control measures, is estimated at €22 billion. The greatest yearly loss of stock is expected to occur in the period 2014-2019, with a peak of three billion euros in 2016, but stabilizing afterwards at 300-800 million euros/year. The reduction in social welfare follows the loss of stock with considerable delay because the yearly harvest from the forest is only 1.8%. The reduction in social welfare for the downstream round wood market is estimated at €218 million in 2030, whereby consumers incur a welfare loss of €357 million, while producers experience a €139 million increase, due to higher wood prices. The societal impact is expected to extend to well beyond the time horizon of the analysis, and long after the invasion has stopped. Conclusions/Significance: Pinewood nematode has large economic consequences for the conifer forestry industry in the EU. A change in spatial resolution affected the calculated directed losses by 24%, but did not critically affect conclusions. [ABSTRACT FROM AUTHOR]

The European dairy industry is facing a number of challenges related to policy changes and global trends that add pressure on their economic performance. This study uses logistic regression to analyze differences in financial and performance indicators between European dairy cooperatives and investor-owned firms. The investigated indicators are profitability, debt, operational efficiency, equity growth, size, and country dummies. The empirical application uses data from 170 European dairy firms. Cooperatives are on average less profitable, operate more efficiently, and have a stronger financial position than investor-owned firms. Using the above-mentioned financial and performance indicators, cooperatives appear to be well equipped to cope with the challenges ahead. [EconLit citations: Q13; M12; C25]. © 2010 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Climate change will affect crop yields and consequently farmers’ income. The underlying relationships are not well understood, particularly the importance of crop management and related factors at the farm and regional level. We analyze the impacts of trends and variability in climatic conditions from 1990 to 2003 on trends and variability in yields of five crops and farmers’ income at farm type and regional level in Europe considering farm characteristics and other factors. While Mediterranean regions are often characterized as most vulnerable to climate change, our data suggest effective adaptation to variable and changing conditions in these regions largely attributable to the characteristic farm types in these regions. We conclude that for projections of climate change impacts on agriculture, farm characteristics influencing management and adaptation should be considered, as they largely influence the potential impacts. [ABSTRACT FROM AUTHOR]