This study focuses on the assessment of the impact of climate change on the efficiency of ambient air aeration and the dynamics of Rhyzopertha dominica, which is a major pest that infests wheat stored in Tunisia. To project future climate conditions for the period 2041–2070, two climate models, namely MPI-ESM1.2 and CNRMCM5.1, were used under two representative concentration pathways (RCP4.5 and RCP8.5). The study examined the historical and projected feasibility of aeration in six natural regions located in northwestern Tunisia, where wheat is the main crop, and investigated the potential consequences of climate change on the dynamics of R. dominica. Using a heat and mass transfer model in an unaerated wheat silo, the temperature and moisture distributions in the grain mass were used to predict the development, reproduction, and survival of R. dominica. The results revealed a decline in favorable aeration hours due to climate change, resulting in an average reduction of 25% across the six regions. This reduction in aerated hours could significantly affect the effectiveness of aeration as a means of pest control. The mean difference comparisons, based on Tukey’s honestly significant difference (HSD) test, revealed a significant effect of climate change on the developmental parameters of R. dominica for the period 2041–2070 compared to the period 1970–1997. According to the insect dynamic model, future climate scenarios are expected to induce changes in the development duration, reproduction, and survival rates of R. dominica. The model predicts a (i) 10–15% extension in the development duration, (ii) 20–30% decrease in reproduction, and (iii) 5–10% decrease in survival relative to the historical period. These results underscore the critical importance of implementing adaptive pest management strategies for stored wheat.

This study evaluates the projected impact of climate change on wheat production in Northwest Tunisia, specifically at Medjez El Beb (36.67 m, 9.74°) and Slougia (36.66 m, 9.6°), for the period 2041–2070. Using the CNRM-CM5.1 and GFDL-ESM2M climate models under RCP4.5 and RCP8.5 scenarios, coupled with the AquaCrop and SIMPLE crop growth models, we compared model outputs with observed data from 2016 to 2020 to assess model performance. The objective was to determine how different climate models and scenarios affect wheat yields, biomass, and growth duration. Under RCP4.5, projected average yields are 7.709 q/ha with AquaCrop and 7.703 q/ha with GFDL-ESM2M. Under RCP8.5, yields are 7.765 tons/ha with AquaCrop and 7.198 q/ha with SIMPLE Crop, indicating that reduced emissions could improve wheat growth conditions. Biomass predictions showed significant variation: in Medjez El Beb, average biomass is 17.99 tons/ha with AquaCrop and 18.73 tons/ha with SIMPLE Crop under RCP8.5. In Slougia, average biomass is 18.90 tons/ha with AquaCrop and 19.04 tons/ha with SIMPLE Crop under the same scenario. Growth duration varied, with AquaCrop predicting 175 days in Medjez El Beb and 178 days in Slougia, while SIMPLE Crop predicted 180 days in Medjez El Beb and 182 days in Slougia, with a standard deviation of ±12 days for both models. SIMPLE Crop demonstrated higher accuracy in predicting growth cycle duration and yield, particularly in Slougia, with mean bias errors of −3.6 days and 2.26 q/ha. Conversely, AquaCrop excelled in biomass prediction with an agreement index of 0.97 at Slougia. Statistical analysis revealed significant yield differences based on climate models and emission scenarios, with GFDL-ESM2M under RCP4.5 showing more favorable conditions. These findings emphasize the importance of model selection and calibration for accurately projecting the agricultural impacts of climate change, and they provide insights for enhancing prediction accuracy and informing adaptation strategies for sustainable wheat production in Northwest Tunisia.

This chapter investigates climate change’s impact on broiler chicken production and reproduction. With climate patterns shifting, poultry farming faces challenges in managing heat stress, ensuring reproductive success, and maintaining overall yield. The physiological responses of broiler chickens to changing environmental conditions, including temperature fluctuations and extreme events, will be explored. Additionally, adaptation strategies and management practices to mitigate these impacts will be discussed. By synthesizing existing literature and empirical evidence, this chapter aims to provide insights into understanding and addressing the complexities of climate change in the broiler chicken industry, offering pathways for sustainable poultry farming in a changing climate.

Coastal zones in arid regions are particularly vulnerable to climate change because of their limited sediment supply and high sensitivity to marine and aeolian forces. This study provides probabilistic projections of coastal evolution for a 130 km segment of the Duba shoreline, Saudi Arabia, a rapidly developing region that includes the NEOM mega-project. An integrated modeling framework was developed by combining a four-decade (1985–2024) diachronic analysis of shoreline evolution from Landsat imagery with a cascade of numerical models. Specifically, climate projections from CMIP6 (under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) were dynamically downscaled using the regional climate models COSMO-CLM and RegCM, which provided boundary conditions for the SWAN hydrodynamic model to simulate the wave dynamics. The SWAN outputs were then used to force the Delft3D morphodynamic model to project future shoreline evolution. A Bayesian framework was applied to systematically quantify and integrate the uncertainties across all modeling steps, enabling robust probabilistic forecasts. Results indicate an accelerated trend of shoreline retreat, with mean Net Shoreline Movement (NSM) by 2100 ranging from −8.1 m under the low-emission SSP1-2.6 scenario to a critical −25.6 m under the high-emission SSP5-8.5 scenario, with 95% confidence intervals reaching −47.9 m. This erosion is mainly driven by a projected relative sea-level rise of up to 48.3 cm (±15.8 cm) and an increase in significant wave height of up to 40% (mean of 1.95 m). By delivering probabilistic rather than deterministic results, this study provides a solid scientific basis to guide sustainable coastal management, inform the design of risk-sensitive infrastructure, and support the development of climate-resilient adaptation strategies in one of the world’s most rapidly transforming coastal regions.

A one-dimensional mathematical model based on the formulation of mass and energy balance in stored grain was used to simulate grains storage conditions. The objective of such simulations was to produce grain ventilation strategies. The model was validated using data obtained from the monitoring of wheat stored in a galvanized steel cylindrical tank with corrugated conical bottom ventilated by perforated distribution pipes. A control strategy based on night time aeration from July to November followed by day time aeration for December to January was applied. Good agreement between the predicted and measured storage conditions has been observed (R2= 0.9698, S.E.= 1.479 °C in average temperature and R2= 0.99, S.E.= 0.00079 kg kg-1for moisture content). Night time grain aeration provided sufficient cooling (temperature near 10 °C in November). However, an 18% grain humidification process was induced. Day time aeration started at the end of November corrected this humidification effect for a grain temperature of 15 °C and a grain moisture content of 15% on dry basis.