Conference Agenda

Artificial intelligence (AI) has made rapid progress in recent years. Specifically, digitalization in agriculture remains one of the most important developments to meet the growing economic, ecological and social demands in the agri-food sector of developing countries. The objective of this work is to analyse the impact of AI adoption on food insecurity in Africa. Using several recent impact analysis methods, we conducted the empirical analysis on a panel of 54 African countries over the period 1990-2022. Several results emerge. First, the adoption of AI on average significantly reduces food insecurity in Africa. The application of AI in the agri-food sector helps to increase the ef iciency, productivity and resilience of food systems. Aside from the optimism of ered by the adoption and use of AI to combat food insecurity, disparities remain between countries. African countries would do well to invest more in high-tech infrastructures but also to increase the number of partnerships between African and international players to capitalise on modern technological knowledge to achieve the goal of hunger zero.

Biomass-based value web concept is becoming recognized more and more as a practical strategy for enhancing household livelihood security in sub-Saharan Africa. There is, however, little data on how it affects the livelihood security of households. Therefore, this study was conducted to further the body of evidence already available on the important impact of the biomass value web on household livelihood security in Africa as a whole and Nigeria in particular. The study's broad objective was to look at the impact of participation in maize biomass value we bon the livelihood security of farming households. A sample of 288 maize farmers in Niger and Nasarawa states of Nigeria were chosen randomly through multi-stage sampling technique, and they were then administered a well-structured questionnaire. Descriptive statistics, a livelihood security analysis, and the Seemingly Unrelated Regression (SUR) technique were the statistical methods employed for the analysis. The study found that, a unit increase in the index of maize usage brings about 10% increase in economic security (p < 0.05) and 17%increase in food security (p < 0.05) of the households. The study concluded that households that innovatively intensified usage within the maize biomass value web have improved livelihood security.

Building on the theory of perceived attributes of innovation and the economic theory of utility maximization, this study evaluates the effects of adopting area yield index-insurance (AYII) on the use of stress-tolerant maize technology. We also identify factors influencing the adoption and intensity of AYII. A stratified random sampling technique was employed in the selection of respondents. Descriptive statistics, a double hurdle model, and logistic regression were used to analyze the data collected. The descriptive statistics showed that the majority of the farmers were male, the average age was 50 and 52 for AYII adopters and non-adopters respectively, and an average farm size of 2 acres and 1.5 acres respectively for AYII adopters and non-adopters. The double hurdle model reveals that factors such as age, marital status, years of education, household size, land tenure, group membership, and training on financial innovations influenced the decision to adopt AYII while only farming experience influenced the intensity of adoption. In addition, the adoption of AYII significantly influenced the use of STM technology. It was therefore recommended that area yield index insurance be promoted through intensive awareness creation and education to ensure wider coverage and extension to other farmers beyond the study area. Similarly, training should also be organized for farmers through collaborative efforts with financial institutions regarding the use of financial institutions to enhance their smooth access to financial innovations.