ORCID Profile
0000-0002-7223-5541
Current Organisations
Institute for Medical Research
,
University of Arid Agriculture
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Publisher: IOP Publishing
Date: 12-2022
Abstract: Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges.
Publisher: PeerJ
Date: 23-07-2019
DOI: 10.7717/PEERJ.7262
Abstract: Maize-soybean relay-intercropping (MS R ) is a famous system of crop production in developing countries. However, maize shading under this system directly affects the light quality and intensity of soybean canopy. This is a challenging scenario in which to implement the MS R system, in terms of varieties selection, planting pattern, and crop management since the duration of crop resource utilization clearly differs. Therefore, this experiment aimed to elucidate the effect of leaf excising treatments from maize top to fully clarify the needs and balance of light quality and intensity of intercrop-soybean under MS R in field conditions. The effects of different leaf excising treatments (T0, no removal of leaves T2, removal of two topmost leaves T4, removal of four topmost leaves T6, removal of six topmost leaves from maize plants were applied at first-trifoliate stage (V 1 ) of soybean) on photosynthetically active radiation transmittance (PAR T ), red to far-red ratio (R:FR), morphological and photosynthetic characteristics and total biomass production at second-trifoliate stage (V 2 ), fifth-trifoliate stage (V 5 ), and flowering-stage (R 1 ) of soybean were investigated through field experiments for 2-years under MS R . As compared to T0, treatment T6 increased the PAR T and R:FR ratio at soybean canopy by 77% and 37% (V 2 ), 70% and 34% (V 5 ), and 41% and 36% (R 1 ), respectively. This improved light environment in T6 considerably enhanced the leaf area index, SPAD values and photosynthetic rate of soybean plants by 66%, 25% and 49% at R 1 , respectively than T0. Similarly, relative to control, T6 also increased the stem diameter (by 29%) but decreased the plant height (by 23%) which in turn significantly increased stem breaking strength (by 87%) by reducing the lodging rate (by 59%) of soybean plants. Overall, under T6, relay-cropped soybean produced 78% of sole soybean seed-yield, and relay-cropped maize produced 81% of sole maize seed-yield. Our findings implied that by maintaining the optimum level of PAR T (from 60% to 80%) and R:FR ratio (0.9 to 1.1), we can improve morphological and photosynthetic characteristics of soybean plants in MS R . Therefore, more attention should be paid to the light environment when considering the sustainability of MS R via appropriate planting pattern selection.
Publisher: Wiley
Date: 24-08-2018
DOI: 10.1111/GCB.14411
Abstract: A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between in idual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all in idual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.
Publisher: Elsevier BV
Date: 10-2014
Publisher: Oxford University Press (OUP)
Date: 13-07-2021
DOI: 10.1111/CEI.13626
Abstract: Primary immunodeficiency diseases refer to inborn errors of immunity (IEI) that affect the normal development and function of the immune system. The phenotypical and genetic heterogeneity of IEI have made their diagnosis challenging. Hence, whole-exome sequencing (WES) was employed in this pilot study to identify the genetic etiology of 30 pediatric patients clinically diagnosed with IEI. The potential causative variants identified by WES were validated using Sanger sequencing. Genetic diagnosis was attained in 46.7% (14 of 30) of the patients and categorized into autoinflammatory disorders (n = 3), diseases of immune dysregulation (n = 3), defects in intrinsic and innate immunity (n = 3), predominantly antibody deficiencies (n = 2), combined immunodeficiencies with associated and syndromic features (n = 2) and immunodeficiencies affecting cellular and humoral immunity (n = 1). Of the 15 genetic variants identified, two were novel variants. Genetic findings differed from the provisional clinical diagnoses in seven cases (50.0%). This study showed that WES enhances the capacity to diagnose IEI, allowing more patients to receive appropriate therapy and disease management.
No related grants have been discovered for MUKHTAR AHMED.