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Financial statements and Independent auditor’s report: December 31, 2023 and 2022
No full text52 page
Community-led last mile youth and women quality centers revolutionizing seed access and empowering rural smallholder farmers in Tanzania
No full text5 page
DArTseq-based SNP markers reveal high genetic diversity among early generation fall armyworm tolerant maize inbred lines
No full textDiversity analysis using molecular markers serves as a powerful tool in unravelling the intricacies of inclusivity within various populations and is an initial step in the assessment of populations and the development of inbred lines for host plant resistance in maize. This study was conducted to assess the genetic diversity and population structure of 242 newly developed S3 inbred lines using 3,305 single nucleotide polymorphism (SNP) markers and to also assess the level of homozygosity achieved in each of the inbred lines. A total of 1,184 SNP markers were found highly informative, with a mean polymorphic information content (PIC) of 0.23. Gene diversity was high among the inbred lines, ranging from 0.04 to 0.50, with an average of 0.27. The residual heterozygosity of the 242 S3 inbred lines averaged 8.8%, indicating moderately low heterozygosity levels among the inbred lines. Eighty-four percent of the 58,322 pairwise kinship coefficients among the inbred lines were near zero (0.00-0.05), with only 0.3% of them above 0.50. These results revealed that many of the inbred lines were distantly related, but none were redundant, suggesting each inbred line had a unique genetic makeup with great potential to provide novel alleles for maize improvement. The admixture-based structure analysis, principal coordinate analysis, and neighbour-joining clustering were concordant in dividing the 242 inbred lines into three subgroups based on the pedigree and selection history of the inbred lines. These findings could guide the effective use of the newly developed inbred lines and their evaluation in quantitative genetics and molecular studies to identify candidate lines for breeding locally adapted fall armyworm tolerant varieties in Ghana and other countries in West and Central Africa
The Powerful Connections of Open Persistent Identifiers (PIDs)
No full textPersistent Identifiers (PIDs) can act as signposts and coordinates, pointing to information sources and showing connections between research and researchers. Individually, each of these identifiers is useful, but their value rises exponentially when they are used collectively in digital workflows, where trusted connections between them can be created and easily shared. Global identifier systems are uniquely positioned to capture mobility and collaboration in research. By leveraging connections between local infrastructures and global information resources, evaluators can map data sources that were previously either unavailable or prohibitively labor-intensive. Persistent identifiers, when open, play a central role in the Open Science framework, as an Open Tools component. We will address the primary open PIDs used in research—for publications, researchers, resources, facilities, research organizations, and funders—as well as demonstrating how they are being used, and how, in combination, they can increase trust in research and the research infrastructure. We will describe how open identifiers, such as ORCID iDs, DOIs and ROR IDs are being embedded in research workflows. In this workshop we will explore how the collective use of Persistent Identifiers (PIDs) in digital workflows enhances the value of individual identifiers, fostering trusted connections between various research elements. We intend to have an event entirely dedicated to persistent identifiers (PIDs) and open science. We will talk about the benefits of persistent identifiers and how their adoption contributes to open science and a more open and robust research ecosystem that strengthens the research ecosystem. Use cases and their benefits will be presented with the interoperability of the different open persistent identifiers. DataCite, ORCID and ROR are open infrastructure organizations focused on connecting research entities and making them findable, uniquely identified, citable, and interoperable. All three are internationally-focused, non-profit, community governed, membership-based organizations that provide foundational open scholarly infrastructure. DataCite, ORCID and ROR all have the same core service: provision of unique, persistent identifiers and associated repositories of metadata and links accessible through open APIs and public datasets. Each set of services is centered on each organization’s focus research entity(s). In conclusion, this workshop aims to empower participants with a deeper understanding of how the collective use of open Persistent Identifiers in digital workflows enhances the interconnectedness of research elements, fostering trust, and contributing to a more open and robust research ecosystem. Learning outcomes expected:-Learn what is a Persistent Identifier (PID); -Increase PID awareness and usage; -Understand how PIDs can help connect the research ecosystem components and be implemented to improve workflows.Ana CardosoArturo Garduño-MagañaNydia LópezPaula Saavedra3:49:3
Post-intervention outcomes in farmer behaviour and crop diversification in Rajshahi, Bangladesh
No full textThis brief summarizes the results from post-intervention outcomes in farmer behavior covering a) preferred cropping patterns among farmers; b) perceived benefits, challenges, and transaction costs of crop diversification, and c) market awareness among trial farmers. Data were collected through face-to-face surveys involving on-farm trail farmers in the Rajshahi district.22 page
Mega demonstrations as a tool for experiential learning: insights from Malawi and Zambia
No full textMega demonstrations embody the proverb "seeing is believing". They are central in creating demand and raising awareness for new technologies in international development. However, evidence of their effectiveness remains context-specific and scarce at best. We interviewed nearly 2,000 farmers in Malawi and Zambia who had attended mega demonstration events, hosted by partners of the ‘Southern Africa Accelerated Innovation Delivery Initiative (AID-I) Rapid Delivery Hub’, to assess learnings, intent to adopt, and actual adoption. The first round, conducted in June 2023, assessed learnings and intent to adopt, while the second round was undertaken around March 2024 to evaluate actual adoption. More than 80% of farmers who attended events at mega demonstrations learnt something new, with more than 50% highlighting new drought-tolerant maize varieties as their preferred choice. Mega demonstrations as a tool for experiential learning: Insights from Malawi and Zambia AID-I 1 Overall, we found a difference of 1- 4 percentage points between intent to adopt and the actual adoption of various technologies. The main drivers of adoption included yield potential, and easy and low cost of implementation. Over 65% of the farmers interviewed went on to adopt drought-tolerant maize varieties during the El-Niño-affected 2023/2024 season. The uptake of other innovations was lower but comparable to the intent to adopt figures. These results suggest a close correlation between the intent to adopt and the actual adoption, and suggest that mega demonstrations play a vital role in facilitating technology uptake. Several areas for improvement were identified, including the need to incorporate organic fertilizer and soil health, bundle demonstrations with other innovations such as advisories, and learning visits to enhance farmer knowledge and adoption.14 page
Exploring metabolomics to innovate management approaches for fall armyworm (Spodoptera frugiperda [J.E. Smith]) infestation in maize (Zea mays L.)
No full textThe Fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith), is a highly destructive lepidopteran pest known for its extensive feeding on maize (Zea mays L.) and other crops, resulting in a substantial reduction in crop yields. Understanding the metabolic response of maize to FAW infestation is essential for effective pest management and crop protection. Metabolomics, a powerful analytical tool, provides insights into the dynamic changes in maize’s metabolic profile in response to FAW infestation. This review synthesizes recent advancements in metabolomics research focused on elucidating maize’s metabolic responses to FAW and other lepidopteran pests. It discusses the methodologies used in metabolomics studies and highlights significant findings related to the identification of specific metabolites involved in FAW defense mechanisms. Additionally, it explores the roles of various metabolites, including phytohormones, secondary metabolites, and signaling molecules, in mediating plant–FAW interactions. The review also examines potential applications of metabolomics data in developing innovative strategies for integrated pest management and breeding maize cultivars resistant to FAW by identifying key metabolites and associated metabolic pathways involved in plant–FAW interactions. To ensure global food security and maximize the potential of using metabolomics in enhancing maize resistance to FAW infestation, further research integrating metabolomics with other omics techniques and field studies is necessary
Genomic prediction for inbred and hybrid polysomic tetraploid potato offspring
No full textPotato genetic improvement begins with crossing cultivars or breeding clones which often have complementary characteristics for producing heritable variation in segregating offspring, in which phenotypic selection is used thereafter across various vegetative generations (Ti). The aim of this research was to determine whether tetrasomic genomic best linear unbiased predictors (GBLUPs) may facilitate selecting for tuber yield across early Ti within and across breeding sites in inbred (S1) and hybrid (F1) tetraploid potato offspring. This research used 858 breeding clones for a T1 trial at Umeå (Norrland, 63°49′30″ N 20°15′50″ E) in 2021, as well as 829 and 671 clones from the breeding population for T2 trials during 2022 at Umeå and Helgegården (Skåne, 56°01′46″ N 14°09′24″ E), respectively, along with their parents (S0) and check cultivars. The S1 and F1 were derived from selfing and crossing four S0. The experimental layout was an augmented design of four-plant plots across testing sites, where breeding clones were non-replicated, and the parents and cultivars were placed in all blocks between the former. The genomic prediction abilities (r) for tuber weight per plant were 0.5944 and 0.6776 in T2 at Helgegården and Umeå, respectively, when T1 at Umeå was used as the training population. On average, r was larger in inbred than in hybrid offspring at both breeding sites. The r was also estimated using multi-environment data (involving at least one S1 and one F1) for T2 performance at both breeding sites. The r was strongly influenced by the genotype in both S1 and F1 offspring irrespective of the breeding site