Drought presents a significant abiotic stress that threatens crop productivity worldwide. Rhizosphere bacteria play pivotal roles in modulating plant growth and resilience to environmental stresses. Despite this, the extent to which rhizosphere bacteria are instrumental in plant responses to drought, and whether distinct cassava (Manihot esculenta Crantz) varieties harbor specific rhizosphere bacterial assemblages, remains unclear. In this study, we measured the growth and physiological characteristics, as well as the physical and chemical properties of the rhizosphere soil of drought-tolerant (SC124) and drought-sensitive (SC8) cassava varieties under conditions of both well-watered and drought stress. Employing 16S rDNA high-throughput sequencing, we analyzed the composition and dynamics of the rhizosphere bacterial community. Under drought stress, biomass, plant height, stem diameter, quantum efficiency of photosystem II (Fv/Fm), and soluble sugar of cassava decreased for both SC8 and SC124. The two varieties\' rhizosphere bacterial communities\' overall taxonomic structure was highly similar, but there were slight differences in relative abundance. SC124 mainly relied on Gamma-proteobacteria and Acidobacteriae in response to drought stress, and the abundance of this class was positively correlated with soil acid phosphatase. SC8 mainly relied on Actinobacteria in response to drought stress, and the abundance of this class was positively correlated with soil urease and soil saccharase. Overall, this study confirmed the key role of drought-induced rhizosphere bacteria in improving the adaptation of cassava to drought stress and clarified that this process is significantly related to variety.

Significant progress has been made in the field of plant genomics, as demonstrated by the increased use of high-throughput methodologies that enable the characterization of multiple genome-wide molecular phenotypes. These findings have provided valuable insights into plant traits and their underlying genetic mechanisms, particularly in model plant species. Nonetheless, effectively leveraging them to make accurate predictions represents a critical step in crop genomic improvement. We present AgroNT, a foundational large language model trained on genomes from 48 plant species with a predominant focus on crop species. We show that AgroNT can obtain state-of-the-art predictions for regulatory annotations, promoter/terminator strength, tissue-specific gene expression, and prioritize functional variants. We conduct a large-scale in silico saturation mutagenesis analysis on cassava to evaluate the regulatory impact of over 10 million mutations and provide their predicted effects as a resource for variant characterization. Finally, we propose the use of the diverse datasets compiled here as the Plants Genomic Benchmark (PGB), providing a comprehensive benchmark for deep learning-based methods in plant genomic research. The pre-trained AgroNT model is publicly available on HuggingFace at https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b  for future research purposes.

Disease resistance gene (R gene)-encoded nucleotide-binding leucine-rich repeat proteins (NLRs) are critical players in plant host defence mechanisms because of their role as receptors that recognise pathogen effectors and trigger plant effector-triggered immunity (ETI). This study aimed to determine the putative role of a cassava coiled-coil (CC)-NLR (CNL) gene MeRPPL1 (Manes.12G091600) (single allele) located on chromosome 12 in the tolerance or susceptibility to South African cassava mosaic virus (SACMV), one of the causal agents of cassava mosaic disease (CMD). A transient protoplast system was used to knock down the expression of MeRPPL1 by clustered regularly interspaced short palindromic repeats-CRISPR-associated protein 9 (CRISPR-Cas9). The MeRPPL1-targeting CRISPR vectors and/or SACMV DNA A and DNA B infectious clones were used to transfect protoplasts isolated from leaf mesophyll cells from the SACMV-tolerant cassava (Manihot esculenta) cultivar TME3. The CRISPR/Cas9 silencing vector significantly reduced MeRPPL1 expression in protoplasts whether with or without SACMV co-infection. Notably, SACMV DNA A replication was higher in protoplasts with lower MeRPPL1 expression levels than in non-silenced protoplasts. Mutagenesis studies revealed that protoplast co-transfection with CRISPR-MeRPPL1 silencing vector + SACMV and transfection with only SACMV induced nucleotide substitution mutations that led to altered amino acids in the highly conserved MHD motif of the MeRPPL1-translated polypeptide. This may abolish or alter the regulatory role of the MHD motif in controlling R protein activity and could contribute to the increase in SACMV-DNA A accumulation observed in MeRPPL1-silenced protoplasts. The results herein demonstrate for the first time a role for a CNL gene in tolerance to a geminivirus in TME3.

Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi-environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics-IITA/Estimating-Realized-Genetic-Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.

The starchy storage roots of cassava are commonly processed into a variety of products, including cassava granulated processed products (gari). The commercial value of cassava roots depends on the yield and quality of processed products, directly influencing the acceptance of new varieties by farmers, processors, and consumers. This study aims to estimate genetic advance through phenotypic selection and identify genomic regions associated and candidate genes linked with gari yield and quality. Higher single nucleotide polymorphism (SNP)-based heritability estimates compared to broad-sense heritability estimates were observed for most traits highlighting the influence of genetic factors on observed variation. Using genome-wide association analysis of 188 clones, genotyped using 53,150 genome-wide SNPs, nine SNPs located on seven chromosomes were significantly associated with peel loss, gari yield, color parameters for gari and eba, bulk density, swelling index, and textural properties of eba. Future research will focus on validating and understanding the functions of identified genes and their influence on gari yield and quality traits.

Bionanocomposites offer a promising solution to the plastic waste crisis. Although tapioca starch shows potential as a bioplastic material, it is characterized by low mechanical properties, poor thermal stability, and high water absorption owing to its hydrophilic nature. To increase the flexibility of the material and reduce the transmission rate of oxygen and water vapor, additives such as fructose and titanium dioxide (TiO2) can be incorporated into the material. TiO2 nanoparticles are commonly utilized in agriculture to enhance nutrient release and promote plant growth. In this study, X-ray diffraction analysis revealed that TiO2 reduced crystal size while increasing the crystallinity of bionanocomposites. Fourier-transform infrared spectroscopy analysis revealed an absorption peak at 3397 cm-1, indicating hydrogen bonding between TiO2 and starch-OH groups, and a peak at 773 cm-1, indicating an increase in the intensity of Ti-O-Ti stretching vibrations with the incorporation of TiO2. Water absorption rate results confirmed that TiO2 addition enhanced bionanocomposite resistance to water vapor and moisture, evidenced by increased tensile strength from 0.11 to 0.49 MPa and Young\'s modulus from 2.48 to 5.26 MPa, as well as decreased elongation at break from 21.46 % to 2.36 % in bionanocomposites with TiO2. Furthermore, with TiO2 addition, the biodegradation rate of the bionanocomposites decreased, which is beneficial for enhancing plant nutrient content.

The presence of salt can impact the fluid phase and gelatinization process of starch granules. The variation in viscosity and rheology models including the Herschel-Bulkley, the Casson model, and the power law, were determined by adding salts before and after starch ultrasonication. Non-isothermal kinetics can be utilized for the mathematical modeling of the gelatinization process and the evolution of the reaction. Unlike Na+ ions, Ca+2 ions notably elevate viscosity. The Casson model accurately predicts viscosity data. Results indicate that the addition of Na+ ions decreases yield stress by up to 60.4 %, while Ca+2 ions increase by up to 100.8 %. Adding Na+ ions decreases the required thermal energy by as much as 49.6 %, while the presence of Ca+2 ions can lead to a substantial increase of up to 337.1 % compared to control samples. The positive ∆G indicates a non-spontaneous gelatinization process. The addition of NaCl promotes a spontaneous reaction, while the addition of CaCl2 increases the Gibbs energy. The changes in entropy are minimal, implying minimal changes in starches\' disorder structure.

The treatment of agroindustrial wastewater using microbial fuel cells (MFCs) is a technological strategy to harness its chemical energy while simultaneously purifying the water. This manuscript investigates the organic load effect as chemical oxygen demand (COD) on the production of electricity during the treatment of cassava wastewater by means of a dual-chamber microbial fuel cell in batch mode. Additionally, specific conditions were selected to evaluate the semi-continuous operational mode. The dynamics of microbial communities on the graphite anode were also investigated. The maximum power density delivered by the batch MFC (656.4 μW m - 2 ) was achieved at the highest evaluated organic load (6.8 g COD L - 1 ). Similarly, the largest COD removal efficiency (61.9%) was reached at the lowest organic load (1.17 g COD L - 1 ). Cyanide degradation percentages (50-70%) were achieved across treatments. The semi-continuous operation of the MFC for 2 months revealed that the voltage across the cell is dependent on the supply or suspension of the organic load feed. The electrode polarization resistance was observed to decreases over time, possibly due to the enrichment of the anode with electrogenic microbial communities. A metataxonomic analysis revealed a significant increase in bacteria from the phylum Firmicutes, primarily of the genus Enterococcus.

Cassava (Manihot esculenta) is among the most important staple crops globally, with an imperative role in supporting the Sustainable Development Goal of \'Zero hunger\'. In sub-Saharan Africa, it is cultivated mainly by millions of subsistence farmers who depend directly on it for their socio-economic welfare. However, its yield in some regions has been threatened by several diseases, especially the cassava brown streak disease (CBSD). Changes in climatic conditions enhance the risk of the disease spreading to other planting regions. Here, we characterise the current and future distribution of cassava, CBSD and whitefly Bemisia tabaci species complex in Africa, using an ensemble of four species distribution models (SDMs): boosted regression trees, maximum entropy, generalised additive model, and multivariate adaptive regression splines, together with 28 environmental covariates. We collected 1,422 and 1,169 occurrence records for cassava and Bemisia tabaci species complex from the Global Biodiversity Information Facility and 750 CBSD occurrence records from published literature and systematic surveys in East Africa. Our results identified isothermality as having the highest contribution to the current distribution of cassava, while elevation was the top predictor of the current distribution of Bemisia tabaci species complex. Cassava harvested area and precipitation of the driest month contributed the most to explain the current distribution of CBSD outbreaks. The geographic distributions of these target species are also expected to shift under climate projection scenarios for two mid-century periods (2041-2060 and 2061-2080). Our results indicate that major cassava producers, like Cameron, Ivory Coast, Ghana, and Nigeria, are at greater risk of invasion of CBSD. These results highlight the need for firmer agricultural management and climate-change mitigation actions in Africa to combat new outbreaks and to contain the spread of CBSD.

This work proposes the pyrolysis of the cassava plant shoot system biomass and a comprehensive chemical characterization of the resulting bio-oil. The highest yields of liquid products were obtained at 600 °C, with 12.6 % bio-oil (organic fraction), which presented the lowest total acid number of 65.7 mg KOH g-1. The bio-oil produced at 500 °C exhibited the highest total phenolic content of approximately 41 % GAE, confirmed by GC/MS analysis (33.8 % of the total area). FT-Orbitrap MS analysis found hundreds of oxygenated constituents in the bio-oils, belonging to the O2-7 classes, as well as nitrogen compounds from the Ny and OxNy classes. Higher pyrolysis temperatures resulted in more oxygenated phenolics (O4-7) undergoing secondary degradation and deoxygenation reactions, generating O2-3 compounds. Additional classes affected were O3-5N2-3, while O1-2N1 presented more stable compounds. These findings show that cassava bio-oils are promising sources of renewable chemicals.