Cassava ( Manihot esculenta Crantz) is a vital tropical root crop that underpins food security, livelihoods, and industrial development for over 800 million people globally, particularly in sub-Saharan Africa. Its resilience to drought and adaptability to marginal environments make it a strategic crop under climate change. However, cassava improvement remains constrained by biological and genetic complexities, including high heterozygosity, clonal propagation, long breeding cycles, and strong genotype × environment (G×E) interactions. Conventional breeding approaches, such as controlled hybridization and phenotypic selection, have historically contributed to yield improvement and disease resistance but are limited by low selection accuracy for polygenic traits and slow genetic gain. Recent advances in molecular genetics and genomics have transformed cassava breeding through the adoption of marker-assisted selection (MAS), genomic selection (GS), and genome-wide association studies (GWAS). These approaches enable the identification of quantitative trait loci (QTLs), prediction of breeding values, and dissection of complex trait architecture, thereby enhancing selection efficiency and accelerating breeding cycles. Statistical tools such as genomic best linear unbiased prediction (G-BLUP), additive main effects and multiplicative interaction (AMMI), and genotype plus genotype-by-environment interaction (GGE) biplot analysis have further improved genotype evaluation and stability analysis across diverse environments. Recent studies demonstrate that genomic selection can reduce cassava breeding cycles from approximately five years to two years while increasing genetic gain. Emerging technologies, including genome editing, high-throughput phenotyping, and artificial intelligence, offer additional opportunities for precision breeding. This review critically synthesizes conventional and modern cassava breeding strategies, highlighting their strengths, limitations, and integration into efficient breeding pipelines. The paper emphasizes the need for data-driven, multi-disciplinary approaches to develop climate-resilient, high-yielding, and quality cassava varieties for sustainable agricultural systems.
Cassava ( Manihot esculenta Crantz ) is a vital staple crop in sub-Saharan Africa, underpinning food security, rural livelihoods, and agro-industrial development. Despite its resilience to marginal soils and drought, cassava productivity is severely constrained by viral and bacterial diseases, notably cassava mosaic disease (CMD), cassava brown streak disease (CBSD), and cassava bacterial light (CBB). Conventional breeding approaches for disease resistance remain limited by long phenotyping cycles, genotype-by-environment interactions, and inadequate spatial integration. This review highlights the emerging role of geospatial genomics, an interdisciplinary framework that integrates Geographic Information Systems (GIS), remote sensing, and genomic tools to accelerate cassava improvement. GIS enables spatial mapping of disease incidence and environmental gradients, while remote sensing technologies such as satellite indices and drone-based hyperspectral imaging provide real-time crop health monitoring. Advances in genomics, including SNP genotyping, genotyping-by-sequencing, and marker-assisted selection, facilitate the identification of resistance loci and predictive breeding. Integrating spatial datasets with genomic information through environmental association analyses enhances understanding of genotype-environment interactions and supports climate-resilient breeding strategies. Applications of geospatial genomics in cassava include disease surveillance, predictive modeling of outbreaks, targeted germplasm deployment, and improved selection efficiency across diverse agro-ecological zones. Through linking environmental variability, disease dynamics, and genetic diversity, geospatial genomics offers a comprehensive pathway to develop disease-resistant cassava varieties, thereby strengthening food security and sustainable agriculture in Africa.
Root and tuber crops like cassava, sweet potato, potato, taro, and yam are important staples in sub-Saharan Africa (SSA), contributing substantially to food security, income generation, and livelihoods of millions of households. As a foundational element of agrifood systems, food safety plays a critical role in ensuring the health and well-being of populations. However, root and tuber agrifood systems in SSA face numerous food safety challenges, including but not limited to cyanogenic glycosides, chemical contaminants, heavy metals, mycotoxins, and pesticide residues that need to be well cataloged. This mini-review identifies and synthesizes key food safety risks associated with root and tuber crops, with a focus on contamination, public health implications, and existing policy and regulatory gaps in SSA. The insights aim to raise and enhance awareness among stakeholders including farmers, consumers, traders, policymakers, and support the development of effective regulatory frameworks. These findings can inform policy formulation and guide strategic investments in food safety infrastructure to strengthen the resilience and competitiveness of SSA root and tuber value chains.
Discrete element model (DEM) parameters are crucial for accurately predicting soil properties and disturbance levels. This study aimed to provide an efficient method for accurately determining DEM parameters for paddy soil. The Hertz-Mindlin and JKR contact models were used to simulate the paddy soil, and the Plackett-Burman, the Steepest Ascent, and the Box-Behnken tests were used to determine the DEM parameters. The accuracy of the established discrete element model was evaluated using actual slump test. The Plackett-Burman test results showed that the soil surface energy, soil-soil rolling friction coefficient, and soil-steel static friction coefficient had a significant impact on the total relative error between the simulation results and the test results. Box-Behnken test optimization results show that the soil surface energy, soil-soil rolling friction coefficient, and soil-steel static friction coefficient are 0.869 J.m², 0.109, and 0.651, respectively. Comparison with actual soil slump test results shows that the calibrated DEM model has an overall relative error of 5.59% and a coefficient of variation of 3.49%. This research can provide a theoretical basis and technical support for subsequent research on soil-machine interaction mechanisms.
Bemisia tabaci is a major pest of cassava in sub-Saharan Africa, causing yield losses through direct feeding and its role in transmitting cassava mosaic disease (CMD). Natural enemies such as lacewings, ladybird beetles, and spiders provide valuable biological control services, yet their interactions with different whitefly developmental stages and plant structural traits remain insufficiently characterized. This study examined the dynamics among natural enemies, whitefly eggs, nymphs, adults, and plant height across 3, 6, 9, and 12 months after planting (MAP) under field conditions. The trial was conducted under natural cassava production conditions during 2020/2021 cropping season at the upland experimental site of the School of Agriculture and Food Sciences, Njala University. A total of 270 cassava genotypes comprising 268 local varieties and 2 improved checks (SLICASS 4 and SLICASS 6) were laid out in an augmented randomized design with four blocks. Results showed that lacewings and spiders strongly tracked nymph and adult whitefly populations, while ladybird beetles showed weaker associations. Principal Component Analysis (PCA) revealed alignment of predators with pest pressure during mid- and late season, whereas plant height exhibited minimal influence. Findings underscore the central role of lacewings and spiders in early and sustained suppression of whitefly populations, highlighting the importance of conservation-based integrated pest management (IPM) strategies. Findings serve as useful guide for conservation biological control as a primary IPM strategy for the enhancement of habitats for effective predators (lacewings and spiders) of the whitefly through reduced pesticide use, ground vegetation retention, intercropping, and maintenance of natural refuge habitats.
