A glasshouse study examined 49 diverse sorghum lines for variation in transpiration efficiency. Three of the 49 lines grown were Sorghum spp. native to Australia; one was the major weed Johnson grass (Sorghum halepense), and the remaining 45 lines were cultivars of Sorghum bicolor. All plants were grown under non-limiting water and nutrient conditions using a semi-automatic pot watering system designed to facilitate accurate measurement of water use. Plants were harvested 56–58 days after sowing and dry weights of plant parts were determined. Transpiration efficiency differed signficantly among cultivars. The 3 Australian native sorghums had much lower transpiration efficiency than the other 46 cultivars, which ranged from 7·7 to 6·0 g/kg. For the 46 diverse cultivars, the ratio of range in transpiration efficiency to its l.s.d. was 2·0, which was similar to that found among more adapted cultivars in a previous study. This is a significant finding as it suggests that there is likely to be little pay-off from pursuing screening of unadapted material for increased variation in transpiration efficiency. It is necessary, however, also to examine absolute levels of transpiration efficiency to determine whether increased levels have been found. The cultivar with greatest transpiration efficiency in this study (IS9710) had a value 9% greater (P < 0·05) than the accepted standard for adapted sorghum cultivars. The potential impact of such an increase in transpiration efficiency warrants continued effort to capture it. Transpiration efficiency has been related theoretically and experimentally to the degree of carbon isotope discrimination in leaf tissue in sorghum, which thus offers a relatively simple selection index. In this study, the variation in transpiration efficiency was not related simply to carbon isotope discrimination. Significant associations of transpiration efficiency with ash content and indices of photosynthetic capacity were found. However, the associations were not strong. These results suggest that a simple screening technique could not be based on any of the measures or indices analysed in this study. A better understanding of the physiological basis of the observed genetic differences in transpiration efficiency may assist in developing reliable selection indices. It was concluded that the potential value of the improvement in transpiration efficiency over the accepted standard and the degree of genetic variation found warrant further study on this subject. It was suggested that screening for genetic variation under water-limiting conditions may provide useful insights and should be pursued.
# In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons ## BackgroundA simulated Multi-Environment Trial (MET) dataset to stimulate the development and comparisons of predictive algorithms to deconvolute genotype-by-environment-by-management interactions in plant breeding. Contains Genomic (SNP & QTL) & Phenotypic Data for Multiple Traits. ### Data##### Genotype *qtl_effects.csv*Information on Causal Genomic Sites (Quantitative Trait Loci - QTL) & their Effect Sizes on Component Traits;- Column 1: QTL Effect Sizes For Propensity To Tiller (ptt)- Column 1: QTL Effect Sizes For Canopy (ams)- Column 1: QTL Effect Sizes For Maturity (mtu)- Column 2: QTL Order- Column 3: Effect Size of each QTL- Column 4: Location Information of each QTL (ChromosomeNumber_WithinChromosomeIndex) *QtlIndex.csv*Information on QTL position for each Component Trait. Position Number connects to Column order in qtl.csv- Column 1: Per Component Trait QTL Index- Column 2: QTL Positions For Propensity To Tiller (ptt)- Column 3: QTL Positions For Canopy (ams)- Column 4: QTL Positions For Maturity (mtu) *qtl.csv*Information on Number of Gene Copies (Alleles) at each QTL for each individual- 1st Column: Individual ID- 2nd Column Onnwards: QTL Allele Counts *markers.csv*Information on the number of Gene Copies (Alleles) at each SNP (Non-Causal Genomic Sites) for each individual- 1st Column: Individual ID- 2nd Column Onwards: SNP Allele Counts ##### Trait Records *trait_data.csv*Trait Records for each individual- Column 1: Genotype ID- Column 2: Phenotype (without error) for Propensity of Tiller (ptt)- Column 3: Phenotype (without error) for Canopy (ams)- Column 4: Phenotype (without error) for Maturity (mtu)- Column 5: Environment/Site where crop was grown- Column 6: Phenotype (without error) for Biomass- Column 7: Phenotype (without error) for Grain Yield- Column 8: Simulated Error for Grain Yield Observations in Column 9- Column 9: Phenotype (with error) for Grain Yield (Heritability=0.99) *trait_data_H2_0.3.csv*Trait Records for each individual- Columns 1- 7: same as trait_data.csv- Column 8: Simulated Error for Grain Yield Observations in Column 9- Column 9: Phenotype (with error) for Grain Yield (Broad Sense Heritability=0.3) *trait_data_H2_0.5.csv*Trait Records for each individual- Columns 1- 7: same as trait_data.csv- Column 8: Simulated Error for Grain Yield Observations in Column 9- Column 9: Phenotype (with error) for Grain Yield (Broad Sense Heritability=0.5) *trait_data_H2_0.8.csv*Trait Records for each individual- Columns 1- 7: same as trait_data.csv- Column 8: Simulated Error for Grain Yield Observations in Column 9- Column 9: Phenotype (with error) for Grain Yield (Broad Sense Heritability=0.8)
Radiation‐use efficiency (RUE; grams of biomass accumulated, divided by total solar radiation intercepted) has proven to be a useful variable for quantifying biomass accumulation by crops. Experimental results and theoretical analysis indicate that differences in RUE exist among species, but that RUE for unstressed conditions is relatively stable within a species. A departure from the general conclusion is that high values of RUE have been reported for conditions where the diffuse proportion of the total radiation is commonly large (e.g., glasshouse‐grown plants). In this paper, a simple theoretical derivation of RUE was examined to quantify potential responses in RUE to variation in the fraction of diffuse radiation. To simulate natural conditions, the absolute amount of incident diffuse radiation was held constant and the amount of the direct radiation component was varied so that both the fraction of diffuse radiation and total radiation were varied. The estimates of RUE for soybean [ Glycine max (L.) Merr.] and maize ( Zea mays L.) increased as the fraction of diffuse radiation increased and the total radiation decreased. These results indicate that plants grown in glasshouses or in other locations with large fractions of diffuse radiation are likely to have greater radiation‐use efficiency than observed under primarily direct radiation.
The CGIAR crop improvement (CI) programs, unlike commercial CI programs, which are mainly geared to profit though meeting farmers' needs, are charged with meeting multiple objectives with target populations that include both farmers and the community at large. We compiled the opinions from >30 experts in the private and public sector on key strategies, methodologies, and activities that could the help CGIAR meet the challenges of providing farmers with improved varieties while simultaneously meeting the goals of: (i) nutrition, health, and food security; (ii) poverty reduction, livelihoods, and jobs; (iii) gender equality, youth, and inclusion; (iv) climate adaptation and mitigation; and (v) environmental health and biodiversity. We review the crop improvement processes starting with crop choice, moving through to breeding objectives, production of potential new varieties, selection, and finally adoption by farmers. The importance of multidisciplinary teams working towards common objectives is stressed as a key factor to success. The role of the distinct disciplines, actors, and their interactions throughout the process from crop choice through to adoption by farmers is discussed and illustrated.
With world commodity markets becoming more competitive and the deregulation of the wheat industry in
Australia during the nineties, advanced knowledge of likely production and its geographical distribution has
become highly sought-after information. During the past 5 years, the Queensland Department of Primary
Industries & Fisheries (DPI&F) has generated shire/state and national yield (t/ha) forecasts for wheat and
sorghum crops on a monthly basis throughout the crop-growing season with appreciable success. However, to
achieve an accurate near real-time production forecast, a real-time estimate of the crop area planted is required.
Generating objective estimates of planted area will allow near real-time crop production estimates, which can
then be used in updating supply chain information at the regional, state and national levels. While there are
alternative methods (e.g. subjective opinions, surveys, censuses, etc.) to derive the required information, the use
of remote sensing (RS) offers more objectivity, timeliness, repeatability and accuracy. Furthermore, the use of
multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (spanning an entire
cropping season) is novel, and has been rarely used in determining crop area planted in targeted agricultural
systems. In this paper, we provided a brief background of regional commodity forecasting in Queensland, and
have reported some preliminary results on the use of digital image processing techniques to determine crop area
planted. More specifically, different multivariate approaches to analysing remote sensing data [i.e. Harmonic
Analysis of Time Series (HANTS) and Principal Component Analysis (PCA)] were compared in determining
winter crop area planted from MODIS imagery for a specific case study in the Darling Downs region,
Queensland. The methodology was validated for the 2003 and 2004 seasons at a shire level by contrasting
aggregated shire total area planted with surveyed ABARE estimates. Finally, the ability of these methods to
discriminate area planted for wheat, barley and chickpea at the shire level was determined. Preliminary results
showed a significant potential to capture total crop area planted at a regional level and a good overall capability
(>95% correct classification) in discriminating between these winter crops.
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