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Occurrence and biodiversity of cyst nematodes in wheat and barley cultivation areas of Uşak Province, Türkiye
No full textPlant parasitic nematodes (PPNs) are one of the major threats to various crops including cereals. In wheat and barley production, the profitability of cultivation heavily depends on the management of cyst nematodes (CNs), especially on Heterodera avenae, H. filipjevi, and H. latipons. In this study, field surveys were conducted in wheat and barley cultivation systems of Uşak Province to reveal the presence and diversity of PPNs. In total, 80 wheat and barley fields were surveyed, and samples (soil and root) were processed for the isolation and identification of PPNs. The PPN genera were identified to the species level based on morphological and morphometric characters. CNs were further analyzed using molecular methods. In total, 17 species belonging to 10 genera were identified. Helicotylenchus Steiner, Pratylenchoides Winslow, Merlinius Siddiqi, and Heterodera Schmidt were the most prevalent and abundant genera. The Shannon diversity index averaged at 2.51 in the wheat and barley fields, indicating a moderate level of biodiversity. The evenness value of 0.798 suggests a high degree of uniformity among migratory PPNs. The results indicate that PPNs have a serious impact on reducing grain yield and quality in the wheat- and barley-growing areas of Uşak Province.947-95
Increasing food diversity and nutritional yield: Evaluating diverse cropping systems. A field study in Chapainawabganj District in Bangladesh
No full textOver the past two decades, Bangladesh has made significant strides in food production, particularly in rice, the country's primary crop (ADB, 2023). However, many people still lack access to a nutritious and diverse diet. Diets remain imbalanced, with rice contributing around 70% of total energy intake (BBS, 2010). The increased production of high-yielding cereals like rice, maize, and wheat has replaced more nutrient-rich cereals like millet, oats, and sorghum. New approaches are needed to produce nutrient-rich foods while using land efficiently. A farmers' participatory research trial was conducted in Chapainawabganj, and a research brief summarizes the results of the nutrition yield of diverse, intensified cropping systems compared to farmers’ common practices from 2022–23 in Chapainawabganj, Bangladesh.16 page
Soil health benchmarking and surveillance in African cropping systems
No full textThere is a paucity of data on the soil health status of various African cropping systems and regions. Against this background, a soil health status characterization project was conceptualized and initiated by CIMMYT and APNI. The project was implemented by sampling sites based in Kenya, Uganda, Tanzania, Zambia, Zimbabwe, Malawi, Morocco, Tunisia and Ghana. This report presents the soil health status of various regions that were targeted by this study.27 page
Expanding the WOFOST crop model to explore options for sustainable nitrogen management: A study for winter wheat in the Netherlands
No full textNitrogen (N) management is essential to ensure crop growth and to balance production, economic, and environmental objectives from farm to regional levels. This study aimed to extend the WOFOST crop model with N limited production and use the model to explore options for sustainable N management for winter wheat in the Netherlands. The extensions consisted of the simulation of crop and soil N processes, stress responses to N deficiencies, and the maximum gross CO2 assimilation rate being computed from the leaf N concentration. A new soil N module, abbreviated as SNOMIN (Soil Nitrogen for Organic and Mineral Nitrogen module) was developed. The model was calibrated and evaluated against field data. The model reproduced the measured grain dry matter in all treatments in both the calibration and evaluation data sets with a RMSE of 1.2 Mg ha−1 and the measured aboveground N uptake with a RMSE of 39 kg N ha−1. Subsequently, the model was applied in a scenario analysis exploring different pathways for sustainable N use on farmers' wheat fields in the Netherlands. Farmers' reported yield and N fertilization management practices were obtained for 141 fields in Flevoland between 2015 and 2017, representing the baseline. Actual N input and N output (amount of N in grains at harvest) were estimated for each field from these data. Water and N-limited yields and N outputs were simulated for these fields to estimate the maximum attainable yield and N output under the reported N management. The investigated scenarios included (1) closing efficiency yield gaps, (2) adjusting N input to the minimum level possible without incurring yield losses, and (3) achieving 90% of the simulated water-limited yield. Scenarios 2 and 3 were devised to allow for soil N mining (2a and 3a) and to not allow for soil N mining (2b and 3b). The results of the scenario analysis show that the largest N surplus reductions without soil N mining, relative to the baseline, can be obtained in scenario 1, with an average of 75%. Accepting negative N surpluses (while maintaining yield) would allow maximum N input reductions of 84 kg N ha−1 (39%) on average (scenario 2a). However, the adjustment in N input for these pathways, and the resulting N surplus, varied strongly across fields, with some fields requiring greater N input than used by farmers
Capacity development for scaling conservation agriculture in smallholder farming systems in Latin America, South Asia, and Southern Africa: exposing the hidden levels
No full textCapacity development is a major pathway for research for development projects to scale innovations. However, both successful scaling and capacity development are held back by a persistent simplistic focus on ‘reaching more end-users’ and training at the individual level, respectively. This study provides examples of the other levels of capacity development: the organizational, cooperation and enabling environment levels. Drawing on four projects implemented by the International Maize and Wheat Improvement Center (CIMMYT) to scale conservation agriculture practices to smallholder farmers, we discovered that these three other levels are less understood, appreciated and reported on than individual training. Trainings are popular to report on because they are simple to plan, quantify, verify, and budget, and success in most projects is measured by the number of individuals reached and trained. There is little awareness and guidance on how to intentionally design and implement projects to address the other capacity development levels. Using a modified framework with clear examples of various types of capacity development activities, project leaders were able to identify and uncover activities that pertain to each of the four levels of capacity development. We argue that project teams must be aware, able, and empowered to invest in the development of capacities of local organizations and the system they operate in. They must be more explicit about the different levels of capacity development, what they mean in their context, and how to create synergies between them. The framework proposed in this paper can serve as a model for initiatives that aim to identify and address capacities at all four levels in order to contribute to large-scale sustainable change.31-5
Development and application of the GenoBaits WheatSNP16K array to accelerate wheat genetic research and breeding
No full textSingle-nucleotide polymorphisms (SNPs) are widely used as molecular markers for constructing genetic linkage maps in wheat. Compared with available SNP-based genotyping platforms, a genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology has become the favored genotyping technology because it is less demanding and more cost effective, flexible, and user-friendly. In this study, a new GenoBaits WheatSNP16K (GBW16K) GBTS array was designed using datasets generated by the wheat 660K SNP array and resequencing platforms in our previous studies. The GBW16K array contains 14 868 target SNP regions that are evenly distributed across the wheat genome, and 37 669 SNPs in these regions can be identified in a diversity panel consisting of 239 wheat accessions from around the world. Principal component and neighbor-joining analyses using the called SNPs are consistent with the pedigree information and geographic distributions or ecological environments of the accessions. For the GBW16K marker panel, the average genetic diversity among the 239 accessions is 0.270, which is sufficient for linkage map construction and preliminary mapping of targeted genes or quantitative trait loci (QTLs). A genetic linkage map, constructed using the GBW16K array-based genotyping of a recombinant inbred line population derived from a cross of the CIMMYT wheat line Yaco“S” and the Chinese landrace Mingxian169, enables the identification of Yr27, Yr30, and QYr.nwafu-2BL.4 for adult-plant resistance to stripe rust from Yaco“S” and of Yr18 from Mingxian169. QYr.nwafu-2BL.4 is different from any previously reported gene/QTL. Three haplotypes and six candidate genes have been identified for QYr.nwafu-2BL.4 on the basis of haplotype analysis, micro-collinearity, gene annotation, RNA sequencing, and SNP data. This array provides a new tool for wheat genetic analysis and breeding studies and for achieving durable control of wheat stripe rust
Chapter 9. Genome-informed discovery of genes and framework of functional genes in wheat
No full textThe complete reference genome of wheat was released in 2018 (IWGSC in Science 361:eaar7191, 2018), and since then many wheats genomic resources have been developed in a short period of time. These resources include resequencing of several hundred wheat varieties, exome capture from thousands of wheat germplasm lines, large-scale RNAseq studies, and complete genome sequences with de novo assemblies of 17 important cultivars. These genomic resources provide impetus for accelerated gene discovery and manipulation of genes for genetic improvement in wheat. The groundwork for this prospect includes the discovery of more than 200 genes using classical gene mapping techniques and comparative genomics approaches to explain moderate to major phenotypic variations in wheat. Similarly, QTL repositories are available in wheat which are frequently used by wheat genetics researchers and breeding communities for reference. The current wheat genome annotation is currently lagging in pinpointing the already discovered genes and QTL, and annotation of such information on the wheat genome sequence can significantly improve its value as a reference document to be used in wheat breeding. We aligned the currently discovered genes to the reference genome, provide their position and TraesIDs, and present a framework to annotate such genes in future.165-18
A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding
No full textDeep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population
Advancements in QTL mapping and GWAS application in plant improvement
No full textIn modern plant breeding, molecular markers have become indispensable tools, allowing the precise identification of genetic loci linked to key agronomic traits. These markers provide critical insight into the genetic architecture of crops, accelerating the selection of desirable traits for sustainable agriculture. This review focuses on the advancements in quantitative trait locus (QTL) mapping and genome-wide association studies (GWASs), highlighting their effective roles in identifying complex traits such as stress tolerance, yield, disease resistance, and nutrient efficiency. QTL mapping identifies the significant genetic regions linked to desired traits, while GWASs enhance precision using larger populations. The integration of high-throughput phenotyping has further improved the efficiency and accuracy of QTL research and GWASs, enabling precise trait analysis across diverse conditions. Additionally, next-generation sequencing, clustered regularly interspaced short palindromic repats (CRISPR) technology, and transcriptomics have transformed these methods, offering profound insights into gene function and regulation. Single-cell RNA sequencing further enhances our understanding of plant responses at the cellular level, especially under environmental stress. Despite this progress, however, challenges persist in optimizing methods, refining training populations, and integrating these tools into breeding programs. Future studies must aim to enhance genetic prediction models, incorporate advanced molecular technologies, and refine functional markers to tackle the challenges of sustainable agriculture