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There is a growing interest in the Mediterranean regions to switch to conservation agriculture (CA) to address climate change and soil deterioration issues. The novelty of this study lies in the quality of the supply chain, from the raw material (durum wheat grain) to the ready-to-sell product (spaghetti), under long-term CA, and using two varieties over two years of study. This study aims to investigate the impact of two soil management systems (SM) (CA after 10/11 (since 2009–2010) years switching vs. conventional tillage (CT)) on grain quality, dough texture profile, and pasta quality of two Tunisian durum wheat varieties (Karim and Monastir) in a 2-year-long experiment (2019 and 2020). The results showed that the SM had a significant impact on the grain quality in both years in terms of protein content and wet gluten, which were, respectively, lower under CA (11.92% vs. 11.15% for protein content) and (18.75% vs. 17.68% for wet gluten) in the wet year. These parameters increased in the dry year but they were higher under CA (15.70% vs. 14.42 ± 0.94% for protein content) and (26.00% vs. 23.20% for wet gluten). These results have, in turn, affected the dough quality (springiness, chewiness, and cohesiveness) and pasta cooking time and decreased the pasta cooking loss and water absorption index. In terms of the variety (V) factor, “Karim” variety in the dry year had a higher protein content and better dough quality than “Monastir” variety, and it reduced the pasta cooking time. In addition, the pasta yellow index (b*) from grains grown under CA was always higher than those in the CT system (23.99 vs. 19.72% and 25.24 vs. 22.19% in 2019 and 2020, respectively). The interaction between SM and V was significant in both years only for the dough hardness and pasta b* parameters. In conclusion, long-term CA may be a crucial solution in the dry season to promote food quality and achieve sustainable agriculture goals.

Due to the climate change situation, water deficit stress is becoming one of the main factors that threatens the agricultural sector in semi-arid zones. Thus, it is extremely important to provide efficient tools of water deficit monitoring and early detection. To do so, a set of Synthetic Aperture Radar (SAR) backscattering and interferometric SAR (InSAR) Sentinel-1 data, covering the period from January to June 2016, are considered over a durum wheat field in Tunisia. We first studied the temporal variation of the InSAR coherence data and the SAR backscattering coefficient as a function of the phenological stage of the wheat. Subsequently, the parameters of the SAR and InSAR coherence images were analyzed with regard to the water stress coefficient and the wheat height variations. The main findings of this study highlight the high correlation (r = 0.88) that exists between the InSAR coherence and the water stress coefficient, on the one hand, and between the backscattering coefficient, the interferometric coherence, and the water deficit coefficient (R2 = 0.95 and RMSE = 14%), on the other hand. When a water deficit occurs, the water stress coefficient increases, the crop growth decreases, and the height variation becomes low, and this leads to the increase of the InSAR coherence value. In summary, the reliability of Sentinel-1 SAR and InSAR coherence data to monitor the biophysical parameters of the durum wheat was validated in the context of water deficits in semi-arid regions.

The monitoring of cereal productions, mainly through yield estimations, has played an important role in providing reliable information to decision makers in order to ensure the proper management of agricultural markets. In this context, remote sensing, which allows the coverage of large areas, is an important source of information that complements those obtained by other methods. In this study, we aim to estimate the wheat yield at an early growth stage (spring season) using only one Radarsat-2 (RS-2) polarimetric image. We propose an empirical statistical relationship between the yield measured in situ and polarimetric parameters extracted from the RS-2 image. The RS-2 image was acquired at the flowering stage as it is proved to be the most appropriate moment for yield prediction. We selected the region of Boussalem in the northwest of Tunisia as the study area. For experimental validation, the yield was determined in situ at the end of the wheat season. Results showed that the polarization ratios are more correlated than the polarimetric parameters with the grain yield with a significant correlation of the HH/VV ratio (r = 0.76) and the HV/VV ratio (r = −0.75), while the most correlated polarimetric parameter was Alpha (r = −0.51). Finally, the multiple regression has led to the development of a three-variable model (HH/VV, HV/HH, and alpha) as the best predictor of the wheat grain yields. Validation results revealed a great potential with a determination coefficient (R2) of 0.58 and root mean squared error (RMSE) of 0.89 t/ha.

This study aimed to determine the emissions of methane (CH4) and nitrous oxide (N2O) from seven Tunisian livestock species and their evolution over eleven consecutive years (2008–2018). The species of animals used were cattle (dairy and others), sheep, goats, camelids, horses, donkeys and mules, and poultry. The estimations of CH4 and N2O emissions were based on the Intergovernmental Panel on Climate Change (IPCC) guidelines for national inventories, using Tier 1 and Tier 2 approaches, with its default emission factors (EFs). The Tier 2 approach was applied only for the calculation of EF to estimate CH4 emissions related to livestock manure management. CH4 emission represented more than 92% of the total greenhouse gas (GHG) from livestock emissions. Moreover, 53% of the total CH4 emissions from livestock were derived from cattle, followed by sheep, goats, other mammals (camelids, horses, mules, and donkeys), and poultry. During the period covered by the study (2008–2018), a slight and continuous decrease of both livestock population and total GHG emissions was observed, mainly in terms of CH4. In mammals, CH4 emissions were greater than N2O emissions, whereas in poultry, N2O emissions were up to 2.6 times greater than CH4 emissions. The aggressive drive of the government to increase cattle and sheep production might affect CH4 emissions in the future. Therefore, periodic estimations of GHG emissions from livestock are required to follow the time trends for more rational decision-making regarding livestock and GHG emissions policies.