The maize commodity is of strategic significance to the South African economy as it is a stable commodity and therefore a key factor for food security. In recent times climate change has impacted on the productivity of this commodity and this has impacted trade negatively. This paper explores the intricate relationship between climatic factors and trade performance for the South African maize. Secondary annual time series data spanning 2001 to 2023, was sourced from an abstract from Department of Agriculture, Land Reform and Rural Development (DALRRD) and World Bank’s Climate Change Knowledge Portal. Autoregressive Distributed Lag (ARDL) cointegration technique was used as an empirical model to assess the long-term and short-term relationships between explanatory variables and the dependent variable. Results of the ARDL model show that, average annual rainfall (β = 2.184, p = 0.056), fertilizer consumption (β = 1.919, p = 0.036), gross value of production (β = 1.279 , p = 0.006) and average annual surface temperature (β = −0.650, p = 0.991) and change in temperature for previous years, (β = −0.650, p = 0.991) and the effects towards coefficient change for export volumes, (β = 0.669, p = 0.0007). In overall, as a recommendation, South African policymakers should consider these findings when developing strategies to mitigate the impacts of some of these climatic factors and implementing adaptive strategies for maize producers.

Over the past 50 years, urban planning documents have been drawn up in sub-Saharan African cities without any convincing results. The study of secondary towns in Chad shows that these planning documents have been hampered by natural and man-made factors. The aim of this study is to determine the factors hindering the implementation of planning documents in the town of Pala in Chad. To carry out the study, a methodological approach (using quantitative and qualitative data) based on a questionnaire and interview survey was deployed for data collection. With a sample of 300 households surveyed, the main conclusions of the study show that all the factors identified, such as water erosion with a rate of 17.7 T/Ha/year, expose the town to various risks. Demographics, on the other hand, represent a lesser and therefore acceptable challenge. As far as exogenous factors are concerned, the level of education of the head of household is a determining factor in the implementation and acceptance of urban planning documents in Pala. Confirmatory factor analysis and the Chi² test revealed that consideration of stakeholders’ needs and their inclusion in the process of drawing up these documents are factors that significantly influence their implementation. In contrast, age, gender and other variables did not reveal any significant anomalies in our analyses. Consequently, future efforts to implement Pala’s planning documents must be based on community participation and awareness of the acceptance of these documents, which are necessary in a process of decentralization and urban planning.

Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.