Environmental Impact of Feeding with Infant Formula in Comparison with Breastfeeding.

Young children have unique nutritional requirements, and breastfeeding is the best option to support healthy growth and development. Concerns have been raised around the increasing use of milk-based infant formulas in replacement of breastfeeding, in regards to health, social, economic and environmental factors. However, literature on the environmental impact of infant formula feeding and breastfeeding is scarce. In this study we estimated the environmental impact of four months exclusive feeding with infant formula compared to four months exclusive breastfeeding in a Norwegian setting. We used life-cycle assessment (LCA) methodology, including the impact categories global warming potential, terrestrial acidification, marine and freshwater eutrophication, and land use. We found that the environmental impact of four months exclusive feeding with infant formula was 35-72% higher than that of four months exclusive breastfeeding, depending on the impact category. For infant formula, cow milk was the main contributor to total score for all impact categories. The environmental impact of breastfeeding was dependant on the composition of the lactating mother's diet. In conclusion, we found that breastfeeding has a lower environmental impact than feeding with infant formula. A limitation of the study is the use of secondary LCA data for raw ingredients and processes.

The effect of climate variability in the efficacy of the entomopathogenic fungus Metarhizium acridum against the desert locust Schistocerca gregaria.

Despite substantial efforts to control locusts they remain periodically a major burden in Africa, causing severe yield loss and hence loss of food and income. Distribution maps indicating the value of the basic reproduction number R0 was used to identify areas where an insect pest can be controlled by a natural enemy. A dynamic process-based mathematical model integrating essential features of a natural enemy and its interaction with the pest is used to generate R0 risk maps for insect pest outbreaks, using desert locust and the entomopathogenic fungus Metarhizium acridum (Synn. Metarhizium anisoliae var. acridum) as a case study. This approach provides a tool for evaluating the impact of climatic variables such as temperature and relative humidity and mapping spatial variability on the efficacy of M. acridum as a biocontrol agent against desert locust invasion in Africa. Applications of M. acridum against desert locust in a few selected African countries including Morocco, Kenya, Mali, and Mauritania through monthly spatial projection of R0 maps for the prevailing climatic condition are illustrated. By combining mathematical modeling with a geographic information system in a spatiotemporal projection as we do in this study, the field implementation of microbial control against locust in an integrated pest management system may be improved. Finally, the practical utility of this model provides insights that may improve the timing of pesticide application in a selected area where efficacy is highly expected.

Weather Patterns Associated with DON Levels in Norwegian Spring Oat Grain: A Functional Data Approach.

Fusarium graminearum is regarded as the main deoxynivalenol (DON) producer in Norwegian oats, and high levels of DON are occasionally recorded in oat grains. Weather conditions in the period around flowering are reported to have a high impact on the development of Fusarium head blight (FHB) and DON in cereal grains. Thus, it would be advantageous if the risk of DON contamination of oat grains could be predicted based on weather data. We conducted a functional data analysis of weather-based time series data linked to DON content in order to identify weather patterns associated with increased DON levels. Since flowering date was not recorded in our dataset, a mathematical model was developed to predict phenological growth stages in Norwegian spring oats. Through functional data analysis, weather patterns associated with DON content in the harvested grain were revealed mainly from about three weeks pre-flowering onwards. Oat fields with elevated DON levels generally had warmer weather around sowing, and lower temperatures and higher relative humidity or rain prior to flowering onwards, compared to fields with low DON levels. Our results are in line with results from similar studies presented for FHB epidemics in wheat. Functional data analysis was found to be a useful tool to reveal weather patterns of importance for DON development in oats.