Tunisia, like most countries in the Middle East and North Africa (MENA) region, has limited renewable water resources and is classified as a water stress country. The effects of climate change are exacerbating the situation. The agricultural sector is the main consumer (80%) of blue water reserves. In this study, to better understand the factors that influence the food water footprint of Tunisian consumers, we used a multiple linear regression model (MLR) to analyze data from 4853 households. The innovation in this paper consists of integrating effects of socio-economic, demographic, and geographic trends on the food consumption water footprint into the assessment of water and food security. The model results showed that regional variations in food choices meant large differences in water footprints, as hypothesized. Residents of big cities are more likely to have a large water footprint. Significant variability in water footprints, due to different food consumption patterns and socio-demographic characteristics, was also noted. Food waste is also one of the determining factors of households with a high water footprint. This study provides a new perspective on the water footprint of food consumption using “household” level data. These dietary water footprint estimates can be used to assess potential water demand scenarios as food consumption patterns change. Analysis at the geographic and socio-demographic levels helps to inform policy makers by identifying realistic dietary changes.

Purpose: This study was conducted in four semi-arid regions in Tunisia – Kef, Siliana, Zaghouan and Kairouan – which have a similar agroecological system based on crop–livestock integration and experience serious soil erosion. The study objective is to estimate the technical efficiency of farmers engaged in mixed crop–livestock systems under conservation agriculture (CLCA) and conventional agriculture in rainfed areas of Tunisia. Design/methodology/approach: The study analyzed data from socio-economic surveys conducted in the Tunisian rainfed areas during the 2020–2021 cropping season. A stochastic frontier analysis was used to estimate farm-level technical efficiency and its main driving factors for both adopter and non-adopter farmers. Findings: The current average technical efficiency level of farmers was 91%. Inputs such as land size and livestock type were significant in increasing farm production. From the estimated coefficients of the inefficiency effects model, five major factors were tested: farmer’s age, education level, dependency ratio, access to credit and extension services. Age and education level positively affected the technical efficiency level. Research limitations/implications: This study has potential limitations. To estimate measures of productivity and technical efficiency, we need output and input measures that are consistently defined and independently measured. The choice of output measure depends on several factors, including data availability and analytical purpose. Since we are dealing with different farms that have different combinations of enterprises, in this reach we approximated the total output by combining the total values of outputs (from both crops and livestock) into one dependent variable to overcome this threat. Practical implications: Providing farmers with accurate and reliable extension information through both conventional and non-conventional technologies (e.g. video, and mobile phones) and improving their educational level through farmers’ field schools are crucial. Combining CLCA practices with improving technical efficiency of farmers in optimal use of inputs through providing training programs, extension services and access to credit for inputs (e.g. small machinery and livestock feed) can contribute substantially to farm productivity, thus enhancing food security in the face of climate change in Tunisian semi-arid areas and other similar contexts. Originality/value: This empirical research provide evidence on how to improve crop-livestock productivity through diversification under conservation agriculture (CA) system, and reduce the effect of climate variability, it is imperative to provide the enabling environment that will facilitate and encourage production of these output combinations among smallholders in rainfed areas.