Soybean productivity in sub-Saharan Africa (SSA) is less than half of the global average yield. To plug the productivity gap, further improvement in grain yield must be attained by enhancing the genetic potential of new cultivars that depends on the genetic diversity of the parents. Hence, our aim was to assess genetic diversity and population structure of elite soybean genotypes, mainly released cultivars and advanced selections in SSA. In this study, a set of 165 lines was genotyped with high-throughput single nucleotide polymorphism (SNP) markers covering the complete genome of soybean. The genetic diversity (0.414) was high considering the bi-allelic nature of SNP markers. The polymorphic information content (PIC) varied from 0.079 to 0.375, with an average of 0.324 and about 49% of the markers had a PIC value above 0.350. Cluster analysis grouped all the genotypes into three major clusters. The model-based STRUCTURE and discriminant analysis of principal components (DAPC) exhibited high consistency in the allocation of lines in subpopulations or groups. Nonetheless, they presented some discrepancy and identified the presence of six and five subpopulations or groups, respectively. Principal coordinate analysis revealed more consistency with subgroups suggested by DAPC analysis. Our results clearly revealed the broad genetic base of TGx (Tropical Glycine max) lines that soybean breeders may select parents for crossing, testing and selection of future cultivars with desirable traits for SSA.

Hyperspectral remote sensing was used to detect stress on potted rice plants caused by the Brown Planthopper (BPH), Nilaparvata lugens (Stål). BPH damage influenced reflectance of rice plants compared to uninfested plants in the visible and near-infrared regions of the electromagnetic spectrum. Correlations between plant reflectance and BPH damage, when plotted against wavelengths, enabled us to identify four sensitive wavelengths, at 1986, 665, 1792 and 500 nm, in relation to BPH stress on rice plants. Based on rice plant reflectance corresponding to the sensitive wavelengths, three hyperspectral indices were developed. The BPH damage showed a positive association with normalized pigment chlorophyll index, and a negative relationship with normalized difference vegetation index and soil adjusted vegetation index. Using rice plant reflectance corresponding to the sensitive wavelengths, a multiple-linear regression model was developed and validated, which would facilitate assessment of BPH damage based on rice plant reflectance, thereby ensuring prompt forewarning to stakeholders.

Soybean rust (SBR), caused by Phakopsora spp., is a major global concern for soybean producers. SBR causing fungi are polycyclic and obligate biotrophs, rendering the study of their biology particularly tedious. Over the past four decades, substantial progress has been made towards understanding the epidemiology of the disease, the identification of sources of resistance, and the mapping of soybean loci conferring resistance to P. pachyrhizi (Rpp genes), since this species is particularly well established and widespread in many soybean growing areas. Although host-plant resistance is generally considered as the most desirable solution from an environmental, economic, and social perspective, other disease control approaches such as agronomic practices and chemical application are also important, and influence rust epidemiology as well as the durability of host plant resistance. This review focusses primarily on genetic aspects of SBR management and summarizes the research in the following areas: SBR symptoms, aetiology, pathogenic variation and population structure of Phakopsora populations, expression of soybean resistance to Phakopsora infection, genetics and molecular diagnostics of host resistance to pathogen, and resistance gene deployment approaches. Finally, the role of multidisciplinary strategies is discussed for achieving higher durability of SBR resistance in soybean.

Climate challenges, along with a projected global population increase of 2 billion by 2080, are intensifying pressures on agricultural systems, leading to biodiversity loss, land use constrains, soil fertility declining, and changes in water cycles, while crop yields struggle to meet the rising food demand. These challenges, coupled with evolving legislation and rapid technology advancements, require innovative sustainable agricultural solutions. By reshaping farmers’ daily operations, real-time data acquisition and predictive models can support informed decision-making. In this context, smart farming (SM) applied to plant breeding can improve efficiency by reducing inputs and increasing outputs through the adoption of digital and data-driven technologies. Examples include the investment on common ontologies and metadata standards for phenotypes and environments, standardization of HTP protocols, integration of prediction outputs into breeding databases, and selection workflows, as well in building multi-partner field networks that collect diverse envirotypes. This review outlines how AI and machine learning (ML) can be integrated in modern plant breeding methodologies, including genomic selection (GS) and genetic algorithms (GAs), to accelerate the development of climate-resilient and sustainably performing crop varieties. While many reviews address smart farming or smart breeding independently, herein, these domains are bridged to provide an understandable strategic landscape by enhancing breeding efficiency.

A field experiment to explore the possibility of improving the productivity and sustainability of basmati rice (Oryza sativa L.) and wheat (Triticum aestivum L. emend. Fiori & Paol.) through organic source was conducted at Karnal during 2004-05 to 2006-07. Rice productivity was at par under inorganic and organic fertilization where farmyard manure (FYM) application was 22.5 t/ha in rice and 20 t/ha or more in wheat. Rice grain-protein content (8.13-8.52%) was at par under all the treatments but the protein yield was significantly higher under inorganic compared to under organic source except under the highest dose (30 tlha in rice and 40 t/ha in wheat) of FYM. Wheat productivity was lower (30.5% on pooled basis) under organic than under inorganic fertilization even under the highest FYM dose. Protein content in wheat increased with increase in the dose of FYM; but the highest pro- tein content (1 1.24%) was recorded under inorganic fertilizer. FYM application for three years significantly im- proved the initial values of soil organic carbon (14.3%), available N (10.7%), P (5.4%) and K (2.6%). Systemis pro- ductivity in terms of rice-equivalent yield was 9.99 t/ha with inorganic fertiliser and 8.88 t/ha with FYM application (30 t/ha in rice and 40 t/ha in wheat). Net returns (Rs 74,170) and benefit : cost ratio (2.1) were significantly higher under inorganic fertilization.

Genetic variability for aluminium tolerance in sunflower (Helianthus annuus L.) | Vivek Kumar Singh, Subhash Chander, Ram Kumar Sheoran, Anu, Om Parkash Sheoran, Ana Luisa Garcia-Oliveira | Agricultural Journals