Variability of Greenhouse Gas Footprints of Field Tomatoes Grown for Processing: Interyear and Intercountry Assessment.

Our study provides an integrated analysis of the variability of greenhouse gas (GHG) footprints of field-grown tomatoes for processing. The global farm-specific data set of 890 observations across 14 countries over a three-year period (2013-2015) was obtained from farms grown under Unilever's sustainable agricultural code. It represents on average 3% of the annual global production of processing tomatoes: insights can be used to help inform corporate sourcing strategies and certification schemes. The median GHG footprint ranged from 18 in Chile to 61 kg CO2-equiv per tonne of tomatoes in India, lower than results reported in other studies. We found that footprints are more consistent within countries than between them. Using linear mixed effect models, we quantified the relative influence of environmental conditions and farm management factors. Key variables were area of production and the method of fertilizer application. GHG footprints decreased with increasing area of production to a threshold of 17.4 ha. Farms using single fertilizer application methods in general had a larger GHG footprint than those using a combination of methods. We conclude that farm management factors should be prioritized for future data collection, and more stringent guidance on acceptable practices is required if greater comparability of outcomes is needed either within a scheme, such as the Unilever's sustainable agriculture code, or between schemes.

Determinants of the Quality of Life in Patients with Drug-Resistant Temporal Lobe Epilepsy: A Comparison of the Results before and after Surgery.

Drug-resistant temporal lobe epilepsy is associated with a reduction in the quality of life of patients. The aim of this study was to compare the quality of life before and after the surgical treatment of epilepsy and to assess factors that may affect the well-being of patients after surgery. The study involved 168 patients with drug-resistant temporal lobe epilepsy. All of them were examined twice: once before and again one year after surgery. Two questionnaires were used in the study: the Quality of Life in Epilepsy Inventory-Patient-Weighted and Hospital Anxiety and Depression Scale and one that collected data on selected demographic and clinical variables. The results showed that patients scored significantly higher in quality of life and lower in depression and anxiety after surgery; however, this only applied to patients with a good outcome of treatment (Engel Class I and Class II). Patients with an unfavorable outcome of surgical treatment (Engel Class III and Class IV) achieved significantly worse results in all examined variables. Correlational analysis showed a relationship between select aspects of quality of life and the level of depression and anxiety, as well as the frequency of seizures and age at epilepsy onset. There was no significant relationship with age, sex, education, or number of prescribed antiepileptic drugs. The study confirms the significant relationship between the quality of life and the effectiveness of surgical treatment, indicating the relationship between patients' well-being and selected clinical indicators.

Estimating greenhouse gas emissions from direct land use change due to crop production in multiple countries.

Greenhouse gas (GHG) emissions from direct land use change (LUC) in GHG footprint studies of crops are often estimated using national land use change statistics, as in many cases the exact location of crop cultivation and land use history is unknown. As such, these studies neglect spatial variability in land use change (amount and configuration) at the sub-national level as well as spatial variability in natural carbon stocks. For this reason, a spatial approach that enables consistent implementation of LUC emissions of crop production at different locations is developed and applied in this study. The dataset of crop production covers 69 crops cultivated on 1885 farms in 33 countries, spanning North and South America, Asia, Australia and Oceania, Europe and Africa, in the year 2014. Of the 1885 farms, 33% (619 farms) were identified to have LUC emissions when estimated at the local scale. LUC emissions of farms, derived using local scale location information, were found to have little correlation with those estimated at coarser spatial scales (such as the province or country level) using the spatial approach in this study or estimated using accounting approaches based on national statistics. Analysis at coarser spatial scales typically overestimated the LUC emissions of crops, as LUC in other regions can heavily influence these estimates. Therefore, it is concluded that local scale LUC emissions better represent local LUC dynamics, thereby improving the reliability of GHG footprint studies.

Greenhouse gas footprints of palm oil production in Indonesia over space and time.

Palm oil, the most widely used vegetable oil, is one of the largest drivers of greenhouse gas (GHG) emissions from global land use and land cover change. Here, we provide fine-resolution (100 m × 100 m) estimates of GHG footprints of current (2015) and potential future scenarios (2030) of crude palm oil (CPO) production in Indonesia. The current estimated average GHG footprint excluding production on Java is 5.7 t CO2 eq t-1 CPO; ranging from 0.7 t CO2 eq t-1 CPO in Hulu Sungai Tengah, Kalimantan to 26.0 t CO2 eq t-1 CPO in Pontianak, Kalimantan, and these vast differences are only discernible at fine spatial scales. The future GHG footprint of Indonesian CPO could be reduced by 42% without compromising increased output by limiting expansion to non-forest and non-peat land. Our fine-scale analysis provides a spatial screening approach to inform new oil palm concessions and sourcing decisions, before more cost-intensive patch analysis and carbon stock assessments are conducted.

Life cycle assessment needs predictive spatial modelling for biodiversity and ecosystem services.

International corporations in an increasingly globalized economy exert a major influence on the planet's land use and resources through their product design and material sourcing decisions. Many companies use life cycle assessment (LCA) to evaluate their sustainability, yet commonly-used LCA methodologies lack the spatial resolution and predictive ecological information to reveal key impacts on climate, water and biodiversity. We present advances for LCA that integrate spatially explicit modelling of land change and ecosystem services in a Land-Use Change Improved (LUCI)-LCA. Comparing increased demand for bioplastics derived from two alternative feedstock-location scenarios for maize and sugarcane, we find that the LUCI-LCA approach yields results opposite to those of standard LCA for greenhouse gas emissions and water consumption, and of different magnitudes for soil erosion and biodiversity. This approach highlights the importance of including information about where and how land-use change and related impacts will occur in supply chain and innovation decisions.