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This study compares the energy return on investment (EROI) of organic and conventional farms in Iceland. It examines which farming method returns the highest amount of edible energy to society relative to the input required. Twenty farms were studied: two organic and 18 conventional. Real data were gathered directly from five farms (including both of the organic farms in the study). Further data from 15 conventional dairy farms of different sizes were collected from a database maintained by the Icelandic Farmers Association. One of the organic farms studied (Org1) was found to have an EROI of 2.68, whereas two conventional farms used as controls for comparison (Con1-a and Con1-b) had EROIs of 0.60 and 0.69, respectively. The second organic farm (Org2) had an EROI of 0.55, versus the control farm ratio of 0.27. On average, large (<170 hectares) conventional dairy farms had an EROI of 0.65, while medium (<70 hectares) and small (<40 hectares) conventional farms had average EROIs of 0.56 and 0.50, respectively. This limited analysis suggests that organic dairy farms may provide better EROIs than conventional farms, but that their dairy yields per hectare are lower.

Purpose In Italy, composting olive mill waste has become a common practice, since it mitigates the environmental problems associated with spreading the waste on land. Compost can be used to prepare growth media for plant nursery cultivation as a substitute for peat, a non-renewable resource whose extraction has long raised environmental concerns. Here, we investigate two common composting procedures--open windrow and static-pile in gas-permeable bags--and compare them to evaluate their environmental impact. Methods We perform a cradle-to-grave life cycle assessment (LCA) in accordance with ISO 14040 and 14044. The LCA considers carbon storage in the soil after 100 years, fugitive greenhouse gas (GHG) emissions, and the impacts avoided by substituting for peat. We use cumulative energy demand, global warming potential (GWP), acidification potential, and eutrophication potential indicators in a contribution analysis and explore how the re-use of olive pits for energy production and reduction of commercial fertilizers improves the environmental balance. We also present a scenario analysis that indicates how parameter fluctuations affect the results. Results and discussion Our study shows that peat's impacts can be significantly reduced from 1162.3 to 96.3 kg CO2-eq/Mg for open windrow compost or 43.1 kg CO2-eq/Mg for static-pile compost in gas-permeable bags. For static-pile composting, the lack of volatile organic compound and ammonia emissions and the detection of oxygen concentrations above 12% vol. suggest fully aerobic conditions. Fugitive greenhouse gas emissions were the most important contributions to the GWP. In the contribution analysis for static-pile composting, the avoidance of compost spreading and the carbon storage effect (due to compost usage) contributed 54% of the overall impacts to GWP and between 21 and 45% to the other indicators. Conclusions This LCA study illustrates how horticulturists can improve their resource management practices by recycling olive mill waste materials. Proper management of composting unit aeration can reduce fugitive GHG emissions.

Shifts in rainfall patterns and increasing temperatures associated with climate change are likely to cause widespread forest decline in regions where droughts are predicted to increase in duration and severity. One primary cause of productivity loss and plant mortality during drought is hydraulic failure. Drought stress creates trapped gas emboli in the water transport system, which reduces the ability of plants to supply water to leaves for photosynthetic gas exchange and can ultimately result in desiccation and mortality. At present we lack a clear picture of how thresholds to hydraulic failure vary across a broad range of species and environments, despite many individual experiments. Here we draw together published and unpublished data on the vulnerability of the transport system to drought-induced embolism for a large number of woody species, with a view to examining the likely consequences of climate change for forest biomes. We show that 70% of 226 forest species from 81 sites worldwide operate with narrow (<1 megapascal) hydraulic safety margins against injurious levels of drought stress and therefore potentially face long-term reductions in productivity and survival if temperature and aridity increase as predicted for many regions across the globe. Safety margins are largely independent of mean annual precipitation, showing that there is global convergence in the vulnerability of forests to drought, with all forest biomes equally vulnerable to hydraulic failure regardless of their current rainfall environment. These findings provide insight into why drought-induced forest decline is occurring not only in arid regions but also in wet forests not normally considered at drought risk.

Ricardian (hedonic) analyses of the impact of climate change on farmland values typically assume additively separable effects of temperature and precipitation. Model estimation is implemented on data aggregated across counties or large regions. We investigate the potential bias induced by such approaches by using a large panel of farm-level data. Consistent with the literature on plant physiology, we observe significant non-linear interaction effects, with more abundant precipitation acting as a mitigating factor for increased heat stress. This interaction disappears when the same data is aggregated in the conventional manner, leading to predictions of climate change impacts which are significantly distorted.

In the last two centuries, land-use change (LUC) has been the most important direct change driver for terrestrial ecosystems. In contrast with the consequent ecosystem degradation, forward-looking spatial policies and target landscape and land-use planning processes are needed from a sustainability perspective. The present paper proposes a framework of action, including different landscape-planning and ecological approaches: from spatial modelling to recognize LUC and build different scenarios, to ecosystem service (ES) assessment to evaluate possible environmental impacts. Three different scenarios were explored: Trend, No Tillage, and Energy crops. The sediment delivery ratio and carbon storage and sequestration ESs were assessed and compared for each scenario. The results show that regional development in line with past trends could lead to further land degradation (with ES value losses, in a decade, greater than 5%). Instead, the two scenarios proposed in compliance with EU policies could bring benefits, if only those related to moderate LUCs and respecting the naturally grass-vegetated land. The aim of the paper is to support decision makers and local communities in the landscape planning landscape planning process. From the local to global scale, guided and shared LUC management allows us to implement sustainable development, based not only on a deep knowledge of the physical environment but also of social and economic issues.

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.