An integrated framework for predicting the risk of experiencing temperature conditions that may trigger late-maturity alpha-amylase in wheat across Australia

Late-maturity alpha-amylase (LMA) is a key concern for Australia’s wheat industry because affected grain may not meet receival standards or market specifications, resulting in significant economic losses for producers and industry. The risk of LMA incidence across Australia’s wheatbelt is not well understood; therefore, a predictive model was developed to help to characterise likely LMA incidence. Preliminary development work is presented here based on diagnostic simulations for estimating the likelihood of experiencing environmental conditions similar to a potential triggering criterion currently used to phenotype wheat lines in a semi-controlled environment. Simulation inputs included crop phenology and long-term weather data (1901–2016) for >1750 stations across Australia’s wheatbelt. Frequency estimates for the likelihood of target conditions on a yearly basis were derived from scenarios using either: (i) weather-driven sowing dates each year and three reference maturity types, mimicking traditional cropping practices; or (ii) monthly fixed sowing dates for each year. Putative-risk ‘footprint’ maps were then generated at regional shire scale to highlight regions with a low (66%) likelihood of experiencing temperatures similar to a cool-shock regime occurring in the field. Results suggested low risks for wheat regions across Queensland and relatively low risks for most regions across New South Wales, except for earlier planting with quick-maturing varieties. However, for fixed sowing dates of 1 May and 1 June and varying maturity types, the combined footprints for moderate-risk and high-risk categories ranged from 34% to 99% of the broad wheat region for South Australia, from 12% to 97% for Victoria, and from 9% to 59% for Western Australia. A further research component aims to conduct a field validation to improve quantification of the range of LMA triggering conditions; this would improve the predictive LMA framework and could assist industry with future decision-making based on a quantifiable LMA field risk

Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery

In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from “stitched” mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches

Genetic basis of sorghum leaf width and its potential as a surrogate for transpiration efficiency

Leaf width was correlated with plant-level transpiration efficiency and associated with 19 QTL in sorghum, suggesting it could be a surrogate for transpiration efficiency in large breeding program. Enhancing plant transpiration efficiency (TE) by reducing transpiration without compromising photosynthesis and yield is a desirable selection target in crop improvement programs. While narrow individual leaf width has been correlated with greater intrinsic water use efficiency in C4 species, the extent to which this translates to greater plant TE has not been investigated. The aims of this study were to evaluate the correlation of leaf width with TE at the whole-plant scale and investigate the genetic control of leaf width in sorghum. Two lysimetry experiments using 16 genotypes varying for stomatal conductance and three field trials using a large sorghum diversity panel (n = 701 lines) were conducted. Negative associations of leaf width with plant TE were found in the lysimetry experiments, suggesting narrow leaves may result in reduced plant transpiration without trade-offs in biomass accumulation. A wide range in width of the largest leaf was found in the sorghum diversity panel with consistent ranking among sorghum races, suggesting that environmental adaptation may have a role in modifying leaf width. Nineteen QTL were identified by genome-wide association studies on leaf width adjusted for flowering time. The QTL identified showed high levels of correspondence with those in maize and rice, suggesting similarities in the genetic control of leaf width across cereals. Three a priori candidate genes for leaf width, previously found to regulate dorsoventrality, were identified based on a 1-cM threshold. This study provides useful physiological and genetic insights for potential manipulation of leaf width to improve plant adaptation to diverse environments

Genetic basis of sorghum leaf width and its potential as a surrogate for transpiration efficiency

Leaf width was correlated with plant-level transpiration efficiency and associated with 19 QTL in sorghum, suggesting it could be a surrogate for transpiration efficiency in large breeding program. Enhancing plant transpiration efficiency (TE) by reducing transpiration without compromising photosynthesis and yield is a desirable selection target in crop improvement programs. While narrow individual leaf width has been correlated with greater intrinsic water use efficiency in C4 species, the extent to which this translates to greater plant TE has not been investigated. The aims of this study were to evaluate the correlation of leaf width with TE at the whole-plant scale and investigate the genetic control of leaf width in sorghum. Two lysimetry experiments using 16 genotypes varying for stomatal conductance and three field trials using a large sorghum diversity panel (n = 701 lines) were conducted. Negative associations of leaf width with plant TE were found in the lysimetry experiments, suggesting narrow leaves may result in reduced plant transpiration without trade-offs in biomass accumulation. A wide range in width of the largest leaf was found in the sorghum diversity panel with consistent ranking among sorghum races, suggesting that environmental adaptation may have a role in modifying leaf width. Nineteen QTL were identified by genome-wide association studies on leaf width adjusted for flowering time. The QTL identified showed high levels of correspondence with those in maize and rice, suggesting similarities in the genetic control of leaf width across cereals. Three a priori candidate genes for leaf width, previously found to regulate dorsoventrality, were identified based on a 1-cM threshold. This study provides useful physiological and genetic insights for potential manipulation of leaf width to improve plant adaptation to diverse environments

Determining broadacre crop area estimates through the use of multi-temporal modis satellite imagery for major Australian winter crops

[Abstract]: Since early settlement, agriculture has been one of the main industries contributing to the livelihoods of most rural communities in Australia. The wheat grain industry is Australia’s second largest agricultural export commodity, with an average value of $3.5 billion per annum. Climate variability and change, higher input costs, and world commodity markets have put increased pressure on the sustainability of the grain industry. This has lead to an increasing demand for accurate, objective and near real-time crop production information by industry. To generate such production estimates, it is essential to determine crop area planted at the desired spatial and temporal scales. However, such information at regional scale is currently not available in Australia. The aim of this study was to determine broadacre crop area estimates through the use of multi-temporal satellite imagery for major Australian winter crops. Specifically, the objectives were to: (i) assess the ability of a range of approaches to using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to estimate total end-of-season winter crop area; (ii) determine the discriminative ability of such remote sensing approaches in estimating planted area for wheat, barley and chickpea within a specific cropping season; (iii) develop and evaluate the methodology for determining the predictability of crop area estimates well before harvest; and (iv) validate the ability of multi-temporal MODIS approaches to determine the pre-harvest and end-of-season winter crop area estimates for different seasons and regions. MODIS enhanced vegetation index (EVI) was used as a surrogate measure for crop canopy health and architecture, for two contiguous shires in the Darling Downs region of Queensland, Australia. Multi-temporal approaches comprising principal component analysis (PCA), harmonic analysis of time series (HANTS), multi-date MODIS EVI during the crop growth period (MEVI), and two curve fitting procedures (CF1, CF2) were derived and applied. These approaches were validated against the traditional single-date approach. Early-season crop area estimates were derived through the development and application of a metric, i.e. accumulation of consecutive 16-day EVI values greater than or equal to 500, at different periods before flowering. Using ground truth data, image classification was conducted by applying supervised (maximum likelihood) and unsupervised (K-means) classification algorithms. The percent correctly classified and kappa coefficient statistics from the error matrix were used to assess pixel-scale accuracy, while shire-scale accuracy was determined using the percent error (PE) statistic. A simple linear regression of actual shire-scale data against predicted data was used to assess accuracy across regions and seasons. Actual shire-scale data was acquired from government statistical reports for the period 2000, 2001, 2003 and 2004 for the Darling Downs, and 2005 and 2006 for the entire Queensland cropping region. Results for 2003 and 2004 showed that multi-temporal HANTS, MEVI, CF1, CF2 and PCA methods achieved high overall accuracies ranging from 85% to 97% to discriminate between crops and non-crops. The accuracies for discriminating between specific crops at pixel scale were less, but still moderate, especially for wheat and barley (lowest at 57%). The HANTS approach had the smallest mean absolute percent error of 27% at shire-scale compared to other multi-temporal approaches. For early-season prediction, the 16-day EVI values greater than or equal to 500 metric showed high accuracy (94% to 98%) at a pixel scale and high R2 (0.96) for predicting total winter crop area planted. The rigour of the HANTS and the 16-day EVI values greater than or equal to 500 approaches was assessed when extrapolating over the entire Queensland cropping region for the 2005 and 2006 season. The combined early-season estimate of July and August produced high accuracy at pixel and regional scales with percent error of 8.6% and 26% below the industry estimates for 2005 and 2006 season, respectively. These satellite-derived crop area estimates were available at least four months before harvest, and deemed that such information will be highly sought after by industry in managing their risk. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. Specific area estimates for wheat, barley and chickpea were, respectively, 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006). Closer investigation suggested that the higher error in 2006 area estimates for barley and chickpea has emanated from the industry figures collected by the government. Area estimates of total winter crop, wheat, barley and chickpea resulted in R2 values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high R2 (0.87) was achieved for total winter crop area estimates in Augusts across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps. This thesis concluded that time-series MODIS EVI imagery can be applied successfully to firstly, determine end-of-season crop area estimates at shire scale. Secondly, capturing canopy green-up through a novel metric (i.e. 16-day EVI values greater than or equal to 500) can be utilised effectively to determine early-season crop area estimates well before harvest. Finally, the extrapolability of these approaches to determine total and specific winter crop area estimates showed good utility across larger areas and seasons. Hence, it is envisaged that this technology is transferable to different regions across Australia. The utility of the remote sensing techniques developed in this study will depend on the risk agri-industry operates at within their decision and operating regimes. Trade-off between risk and value will depend on the accuracy and timing of the disseminated crop production forecast

Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields

Assessing the relationship between shire winter crop yield and multi-temporal MODIS NDVI and EVI images

Australian researchers have been developing robust yield simulation models, based mainly on the crop growth response to the rainfall amount and distribution during the crop season. However, better knowledge of spatial distribution of yields in the production regions can be estimated by the use of remote sensing techniques. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available at MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland, Australia. Five years (2000 to 2004) of 250m, 16-day composite of MODIS NDVI and EVI images were used during the winter crop season (April to November). For these shires, a mask of cropping area was applied by using a land use classification map derived from Landsat TM. Multi-temporal profiles of the NDVI and the EVI imagery for each crop season were displayed and analysed. Wheat and barley yields, provided by the Australian Bureau of Statistics, were correlated to the maximum and to the integrated crop season values for both NDVI and EVI at the shire level. The temporal VI profiles were quite similar in Jondaryan and Pittsworth, with minimum values in April, May and June, a peak in August, and decreasing until November. Bigger differences were found between years. The correlation analysis between the winter crop yields and VIs pointed out that EVI images were better than the NDVI ones. Most part of the coefficients was statistically significant when using EVI spectral information from the Integrated and Maximum results. The results presented in this paper showed that the VI images are a powerful tool to assess near real-time biomass status

Estimating crop area using seasonal time series of Enhanced Vegetation Index from MODIS satellite imagery

[Abstract]: Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state and national scales. There is now an increasing drive from industry for more accurate and cost effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. A range of multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April to November) were investigated to estimate crop area for wheat, barley, chickpea and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period, (ii) Harmonic Analysis of the Time Series (HANTS) of the EVI values, and (iii) Principal Component Analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at a pixel scale (>98% correct classification) for identifying overall winter cropping at pixel scale. Discrimination among crops was less accurate, however. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire-scale, the result contradicted the poor pixel scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped area estimates before harvest

Spying on the winter wheat crop - generating objective planted area and crop production estimates using MODIS imagery

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

A regional commodity forecasting system for major crops in Australia

Queensland Department of Primary Industries has developed a regional commodity forecasting system, which integrates a shire-based stress-index wheat model with seasonal climate forecasts based on the El Nino Southern Oscillation (ENSO). It allows the examination of the likelihood of exceeding the long-term median shire yield associated with different season types at the beginning of the cropping season. This system is now run operationally for Queensland by updating the projection each month based on the actual rainfall that has occurred and any change in the ENSO phase from month to month. Although this system was principally designed to inform government in Queensland of any areas that might be more likely to experience poor crops in any year it also serves as a regional commodity forecasting system. The information generated provides an alert for exceptional circumstance issues associated with potential drought in Queensland. However, anecdotal information received from marketing agencies based on their experience with the 2000 regional wheat outlook showed that using this seasonal crop forecasting system in their decision-making processes could add value to their current approaches. Possible decisions to be taken when the outlook is for “likely to be drier (wetter) than normal” are, for instance, forward buying (selling) of grain or shifting of resources from good yielding areas to poor yielding areas