International Crops Research Institute for the Semi-Arid Tropics
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Mechanisms of pre-attachment Striga resistance in sorghum through genome-wide association studies
No full textWitchweeds (Striga spp.) greatly limit production of Africa’s most staple crops. These parasitic plants use strigolactones (SLs)—chemical germination stimulants, emitted from host’s roots to germinate, and locate their hosts for invasion. This information exchange provides opportunities for controlling the parasite by either stimulating parasite seed germination without a host (suicidal germination) or by inhibiting parasite seed germination (pre-attachment resistance). We sought to determine genetic factors that underpin Striga pre-attachment resistance in sorghum using the genome wide association study (GWAS) approach. Results revealed that Striga germination was associated with genes encoding hormone signaling functions, e.g., the Novel interactor of jaz (NINJA) and, Abscisic acid-insensitive 5 (ABI5). This pointed toward abscisic acid (ABA) and gibberellic acid (GA) as probable determinants of Striga germination. To test this hypothesis, we conditioned Striga using: ABA, ABA + its inhibitor fluridone (FLU), GA or water. Unexpectedly, Striga conditioned with FLU germinated after 4 days without SL. Upon germination stimulation using sorghum root exudate or the synthetic SL GR24, we found that ABA conditioned seeds had above 20-fold reduction in germination. Conversely, FLU conditioned seeds recorded above 20-fold increase in germination. Conditioning with GA reduced Striga seed germination 1.5-fold only in the GR24 treatment. Germination assays using seeds of a related parasitic plant (Alectra vogelii) showed similar degrees of stimulation and reduction of germination by the hormones further affirming the hormonal crosstalk. Our findings have far-reaching implications in the control of some of the most noxious pathogens of crops in Africa
Salient Findings on Host Range, Resistance Screening, and Molecular Studies on Sterility Mosaic Disease of Pigeonpea Induced by Pigeonpea sterility mosaic viruses (PPSMV-I and PPSMV-II)
Get PDFTwo distinct emaraviruses, Pigeonpea sterility mosaic virus-I (PPSMV-I) and Pigeonpea sterility mosaic virus-II (PPSMV-II) were found to be associated with sterility mosaic disease (SMD) of pigeonpea [Cajanus cajan (L.) Millsp.]. The host range of both these viruses and their vector are narrow, confined to Nicotiana benthamiana identified through mechanical transmission, and to Phaseolus vulgaris cvs. Top Crop, Kintoki, and Bountiful (F: Fabaceae) through mite transmission. A weed host Chrozophora rottleri (F: Euphorbiaceae) was also infected and tested positive for both the viruses in RT-PCR. Among the wild Cajanus species tested, Cajanus platycarpus accessions 15661, 15668, and 15671, and Cajanus scarabaeoides accessions 15683, 15686, and 15922 were infected by both the viruses and mite vector suggesting possible sources of SMD inoculum. Though accession 15666 of C. platycarpus, 15696 of C. scarabaeoides, and 15639 of Cajanus lanceolatus were infected by both the viruses, no mite infestation was observed on them. Phylogenetic analysis of nucleotide sequences of RNA-1 and RNA-2 of PPSMV-I and PPSMV-II isolates in southern India revealed significant divergence especially PPSMV-II, which is closely related to the Fig mosaic virus (FMV) than PPSMV-I. In multilocation testing of pigeonpea genotypes for their broad-based resistance to SMD for two consecutive years, genotypes ICPL-16086 and ICPL-16087 showed resistance reaction (<10% incidence) in all three locations studied. Overall, the present study gives a clear idea about the host range of PPSMV-I and PPSMV-II, their molecular relationship, and sources of resistance. This information is critical for the development of reliable diagnostic tools and improved disease management strategies
Illustration of key morphological characteristics of Phytophthora cajani-pathogen of phytophthora blight of pigeonpea
Get PDFBackground
Phytophthora cajani causing the Phytophthora blight (PB) disease of pigeonpea. The disease will rampant during excessive rainfall coupled with hot and humid weather during the cropping season. The present study on micro and macro morphological characteristics can contribute to the identification and specification of biology of Phytophthora spp. There are no detailed studies concerning the characterization of the P. cajani are available with this backdrop the present investigation was taken.
Methods
Phytophthora cajani was isolated on V-8 PARP medium, whereas stimulation of zoospores and sporangia was done using the diluted tomato juice broth. Micro and macro morphological characteristics of P. cajani were studied using micrometry and Olympus CX41 phase-contrast microscope.
Result
The pathogen was homothallic with amphigynous antheridium and oogonium and able to produce oospore in vitro. Sporangium was nonpapillate, noncaducous, oviod-obpyriform shape. Further, the macro morphological characteristics like mycelial radial growth and colony type were studied. The colony characteristics were dull white, flat and rosette pattern. Other culture characteristics like optimum temperature and RH were mostly consistent with those reported former
New high-yielding, stress-resilient, and nutritious crop varieties
Get PDFThe Africa RISING research for development agenda is guided by the principles of sustainable intensification (SI) and farming systems. SI refers to efficient use of resources for agriculture that results in increased productivity on the same amount of land (Pretty, 1997; Reardon et al., 1996). A farming system refers to a population of individual farm systems that may have widely different resource bases, enterprise patterns, household livelihoods, and constraints (Giller, 2013). Africa RISING focuses on efficient use of
resources for agriculture, to produce more food on the same amount of land, but with reduced negative environmental or social impacts. This approach is necessary given the diverse farming systems of East and Southern Africa (ESA), which are predominantly smallholder-based
Zinc and iron biofortification in pearl millet cultivars as influenced by different fertifortification strategies in semi arid tropics
No full textTwo field experiments were conducted during Kharif 2018 and 2019 on clay loam soils at Zonal Agricultural and Horticultural Research Station, Babbur farm, Hiriyur, Karnataka to evaluate the performance of pearl millet cultivars by different zinc (Zn) and iron (Fe) fertifortification methods. Micronutrient (Zn and Fe) management strategies were employed in main plots that include application of recommended NPK, along with recommended NPK supply of Fe and Zn through soil, foliar application and FYM enrichment along with plant growth promoting rhizobacteria (PGPR). Three pearl millet cultivars [Variety - ICTP 8203 Fe (Dhanshakti), Hybrid - ICMH 1202 and local cultivar (variety - WCC 75)] were considered in sub plots laid in split plot design with three replications. Three methods of micronutrient application tested improved the grain Fe and Zn concentrations of both biofortified cultivars Dhanshakti and Hybrid along with grain yield. Enrichment of FYM with Fe and Zn resulted in higher concentration and uptake of Fe and Zn than other modes of micronutrient application. The hybrid ICMH 1202 had obtained significantly higher grain yield (2203 kg ha−1), grain micronutrient concentration of pearl millet (50.52 and 83.88 mg kg−1 Zn and Fe, respectively) with the application of enriched FYM + PGPR along with recommended NPK and found the best
Evaluating dimensionality reduction for genomic prediction
Get PDFThe development of genomic selection (GS) methods has allowed plant breeding programs to select favorable lines using genomic data before performing field trials. Improvements in genotyping technology have yielded high-dimensional genomic marker data which can be difficult to incorporate into statistical models. In this paper, we investigated the utility of applying dimensionality reduction (DR) methods as a pre-processing step for GS
methods. We compared five DR methods and studied the trend in the prediction accuracies of each method as a function of the number of features retained. The effect of DR methods was studied using three models that involved the main effects of line, environment, marker, and the genotype by
environment interactions. The methods were applied on a real data set containing 315 lines phenotyped in nine environments with 26,817 markers each. Regardless of the DR method and prediction model used, only a fraction of features was sufficient to achieve maximum correlation. Our results underline the usefulness of DR methods as a key pre-processing step in GS models to improve computational efficiency in the face of ever-increasing size of genomic data
Editorial: Biotechnological and genomic approaches for enhancing agronomic performance of crops
Get PDFEvidencing what works in developing new market opportunities for GLDC crops
Get PDFThis paper aims to cast light on the effectiveness of interventions to promote the use of Grain Legumes and Dryland Cereal (GLDC) crops by consumers and industry. Underpinning this activity is the hypothesis that interventions which promote GLDC crops (particularly in new/ non-traditional uses) will create new and or more profitable and scalable market opportunities for smallholder farmer, increasing their income and helping drive technology adoption. These hypotheses remain largely untested, with no systematic evidence base of the sort of “promotion” activities that can create enduring, inclusive market opportunities at scale for the small holder sector producing GLDC crops. Here, we examine six case studies: i) Global competition: sorghum beer in Kenya, ii) The power of incentives: sflatoxin control for groundnuts in Malawi, iii) Marketing modernity: Smart Food in India and Eastern Africa, iv) The Politics of pricing: sweet sorghum as a biofuel in India, v) Too many moving parts? precooked beans in Uganda and Kenya, and vi) Market-led plant breeding: pigeonpea in Eastern and Southern Africa. The analysis of these case studies will allow to draw five key lessons on what works and why, and what causes failure, when implementing interventions aimed at developing new market opportunities for the GLDC crops
Detecting Soil pH from Open Source Remote Sensing Data: A Case Study of Angul and Balangir districts, Odisha State
Get PDFInternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT) is
implementing ‘Odisha Bhoochetana’, an agricultural development project in Angul and
Balangir districts in India. Under this project, soil health improvement activity was
initiated by collecting soil samples from selected villages of the districts. Soil
information before sowing helps farmers not only to choose a crop but also in planning
crop nutritional inputs. Soil sampling, collection, and analysis is a costly and laborintensive
activity that cannot cover the entire farmlands, hence it was conceived to use
high-speed open-source platforms like Google Earth Engine in this research to
estimate soil characteristics remotely using high-resolution open-source satellite data.
The objective of this research was to estimate soil pH from Sentinel1, Sentinel 2, and
Landsat satellite-derived indices; Data from Sentinel 1, Sentinel 2, and Landsat
satellite missions were used to generate indices and as proxies in a statistical model to
estimate soil pH. Step-wise multiple regression, Artificial Neural networks (ANN) and
Random forest (RF) regression, and Class-wise random forest were used to develop
predictive models for soil pH. Step-wise multiple regression, ANN, and RF regression
are single class models while class-wise RF models are an integration of RF-Acidic,
RF-Alkaline, and RF- Neutral models (based on soil pH). The step-wise regression
model retained the bands and indices that were highly correlated with soil pH. Spectral
regions that were retained in the step-wise regression are B2, B11, Brightness Index,
Salinity Index 2, Salinity Index 5 of Sentinel 2 data; VH/VV index of Sentinel 1 and
TIR1 (thermal infrared band1) Landsat with p-value <0.001. Amongst the four statistical
models developed, the class-wise RF model performed better than other models with a
cumulative R 2 and RMSE of 0.78 and 0.35 respectively. The better performance of class-wise RF models over single class models can be attributed to different spectral
characteristics of different soil pH groups. Though neural networks performed better
than the stepwise multiple regression model, they are limited to a regression while the
random forest model was capable of regression and classification. The large tracts of
acidic soils (datasets) in the study area contributed to the training of the model
accordingly leading to neutral and alkaline soils that were misclassified hindering the
single class model performance. However, the class-wise RF model was able to
address this issue with different models for different soil pH classes dramatically
improving prediction. Our results show that the spectral bands and indices can be used
as proxies to soil pH with individual classes of acidic, neutral, and alkaline soils. This
study has shown the potential in using big data analytics to predict soil pH leading to
the accurate mapping of soils and help in decision support
Recent advancements in CRISPR/Cas technology for accelerated crop improvement
Get PDFThe likelihood of reduced agricultural production due to highly turbulent climatic conditions increases as the
global population expands. The second paradigm of stress-resilient crops with enhanced tolerance and increased productivity against various stresses is paramount to support global production and consumption equilibrium. Although traditional breeding approaches have substantially increased crop production and yield, effective strategies are anticipated to restore crop productivity even further in meeting the world’s increasing food demands. CRISPR/Cas, which originated in prokaryotes, has surfaced as a coveted genome editing tool in recent decades, reshaping plant molecular biology in unprecedented ways
and paving the way for engineering stress-tolerant crops. CRISPR/Cas is distinguished by its efficiency, high target specificity, and modularity, enables precise genetic modification of crop plants, allowing for the creation of allelic variations in the germplasm and the development of novel and more productive agricultural practices. Additionally, a slew of advanced biotechnologies
premised on the CRISPR/Cas methodologies have augmented fundamental research and plant synthetic biology
toolkits. Here, we describe gene editing tools, including CRISPR/Cas and its imitative tools, such as base and prime editing, multiplex genome editing, chromosome engineering followed by their implications in crop genetic improvement. Further, we comprehensively discuss the latest developments of CRISPR/Cas technology including CRISPR-mediated gene drive, tissue-specific genome editing, dCas9 mediated epigenetic modification and programmed self-elimination of transgenes in plants. Finally, we highlight the applicability and scope of advanced CRISPR-based techniques in crop genetic improvement