Within the framework of an increasing water scarcity, fostered by the climate change, in this study we investigate the environmental impact associated with current food consumption by means of the Water footprint indicator focusing on the case of Italy. For the analysis of the real food consumption and its impact, we selected the Mediterranean diet as a benchmark, since previous evidence shows that Italians are shifting their dietary habits away from the traditional Mediterranean Diet in favor of dietary patterns rich in animal-based products, especially meat. To promote more sustainable food choices, we applied a Sustainable Diet Model to analyze alternative diets that can reduce the Water footprint associated with food consumption. The results of the analysis show that by adopting slight changes in the consumption of certain categories of foods, it is possible to reduce the Water footprint of diets. The findings of this research are useful for supporting policies for the promotion of sustainable food consumption that would lead to a reduction in the exploitation of a scarce resource such as water, improving the allocation of this resource and achieving the objectives of the 2030 Agenda.

Mexico is going through an environmental and nutritional crisis related to unsustainable dietary behaviours. Sustainable diets could solve both problems together. This study protocol aims to develop a three-stage, 15-week mHealth randomised controlled trial of a sustainable-psycho-nutritional intervention programme to promote Mexican population adherence to a sustainable diet and to evaluate its effects on health and environmental outcomes. In stage 1, the programme will be designed using the sustainable diets, behaviour change wheel and capability, opportunity, motivation, and behaviour (COM-B) models. A sustainable food guide, recipes, meal plans and a mobile application will be developed. In stage 2, the intervention will be implemented for 7 weeks, and a 7-week follow-up period in a young Mexican adults (18–35 years) sample, randomly divided (1:1 ratio) into a control group (n 50) and an experimental group (n 50), will be divided into two arms at week 8. Outcomes will include health, nutrition, environment, behaviour and nutritional-sustainable knowledge. Additionally, socio-economics and culture will be considered. Thirteen behavioural objectives will be included using successive approaches in online workshops twice a week. The population will be monitored using the mobile application consisting of behavioural change techniques. In stage 3, the effects of the intervention will be assessed using mixed-effects models on dietary intake and quality, nutritional status, physical activity, metabolic biomarkers (serum glucose and lipid profile), gut microbiota composition and dietary water and carbon footprints of the evaluated population. Improvements in health outcomes and a decrease in dietary water and carbon footprints are expected.

At a global level, dairy cow production systems (DCPS) are important sources of nourishment and profits, but they generate environmental impacts such as overexploitation of different resources including water, lands and fossil energy. Quantification of water and carbon footprint to define mitigation strategies and a more rational use of natural resources, is a reiterated claim. The aim of this study was to perform an economic evaluation of the environmental impact of the DCPS from the Comarca Lagunera, Mexico (24°N, 102°W, 220 mm, hot-semiarid climate) We contrasted the economic value (EV) generated by the DCPS with respect to the economic costs (EC) due to the greenhouse gas emissions (GHGE) and the water footprint (WFP) of this DCPS. While quantifications of GHGE considered those proposed by the Intergovernmental Panel on Climate Change, the WFP involved the use of blue, gray and green water by the DCPS and related activities. Quantification of the EC of WFP considered an international average price of water. In the year 2017, the Comarca Lagunera registered a dairy cow inventory of 493 144 heads, with 227 142 lactating cows, which produced 2386 million liters of milk per year with an annual average EV of €525.3 million. The EC (€, millions) generated by the GHGE and WFP were €311.8 and €11 980.7, respectively, with a total EC of € 12 292.5 million. When the EV of milk production and the total environmental EC are compared, the contrast demonstrates not only the noteworthy environmental impact but also the significant and senseless biological and EC. In addition, having a large dairy cow concentration creates pollution concerns and the DCPS transfers both nutrients and water resources from an ecologically vulnerable arid region. Therefore, some mitigation strategies such as, better cow genotype, feed and manure management combined with the production of forages and grains in a different geographical region are suggested to promote an optimum use of water in order to uphold the social, economic and biologic sustainability of the Comarca Lagunera, Mexico.

Water scarcity is among the main challenges making vulnerable the livestock farming systems in drylands. The water footprint (WF) indicator was proposed as a metric to measure the impacts of livestock production on freshwater resources. Therefore, this study aimed to assess water use in five different Tunisian sheep production systems using the Water Footprint Network methodology. The primary data were obtained from 1050 sheep farms located in 13 Tunisian provinces. A multivariate analysis was performed to characterize the different farming systems. A validation step of the WF modeled values of sheep meat was conducted in 12 sheep farms belonging to two different farming systems. This was done through year-round monitoring of on-farm practices using water metres and recording equipment’s taking into account the direct and indirect water use. The typology analysis came up with five sheep farming systems that are the mixed sheep-cereal (MSC), the agro-sylvo-pastoral (ASP), the agro-pastoral (AP), the extensive agro-pastoral (EAP) and the mixed sheep-olive tree farming systems. The WF of sheep meat produced under the target farming systems ranged from 8654 to 13 056 l/kg live weight. The evaluation of WF of five different sheep production systems figured out that sheep raised under the EAP farming system had the greatest WF per ton of live animal. However, the ASP farming system exhibited the lowest WF. Water used to grow feedstuffs for sheep production accounts for 98% of the total WF of sheep. The green WF accounts for more than 92% of the total WF in all farming systems. Results of monitoring water use at farm scale show that the modeled values of WF are overestimated by an average of 23.3% and 24.1% for the selected farms assigned to the MSC and AP farming systems, respectively. Water use for sheep production is high in most of the Tunisian farms. Therefore, the general assumption that ‘meat production is a driver of water scarcity’ is supported and should be considered as an important focal point in agricultural and water policies. Particular attention should be given to forage crops with low WFs and high contribution to dry matter to provide ration with low WF. The efficient use of green water along the meat value chain is essential to minimize the depletion of blue water resources and to reduce the economic dependency on virtual water through the import of feedstuffs.

A probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

In the current regional-scale study, the model DSSAT CROPGRO was applied in order to simulate the cultivation of industrial tomato and to estimate the green water (GW), blue water (BW), blue water requirement (BWR) and water footprint (WFP) through a dual-step approach (with and without full irrigation). Simulation covered a period of 30 years for three climate scenarios including a reference period and two future scenarios based on forecast global average temperature increases of 2 and 5 °C. The spatial patterns of indicators relating to the whole territory of Puglia region (Southern Italy), characterized by the high evaporative demand of the atmosphere, are discussed and analysed. Considering the climatic pattern, the analysis was developed for three areas (Northern, Central and Southern). Future scenarios affected all indicators significantly, particularly the Northern area, characterized by higher temperature and rainfall anomalies. Under the A5 scenario, compared with the baseline, this area was forecast to have a large increase of BW (+30%) and reduction in yield (−20%). As a consequence, the BWR and WFP were predicted to increase dramatically, up to 40 and >65%, respectively. On the other hand, Central and Southern areas, with lower anomalies of temperature and rainfall, were forecast to be less vulnerable to climate change. The distributed analysis performed could be important for water policy, allowing most efficient allocation of scarce water resources and concentrating them where the WFP is lowest, or in other words, water use efficiency is highest.