NIR model development and robustness in prediction of melon fruit total soluble solids

Near infrared spectroscopy (NIRS) can be used for the on-line, non-invasive assessment of fruit for eating quality attributes such as total soluble solids (TSS). The robustness of multivariate calibration models, based on NIRS in a partial transmittance optical geometry, for the assessment of TSS of intact rockmelons (Cucumis melo) was assessed. The mesocarp TSS was highest around the fruit equator and increased towards the seed cavity. Inner mesocarp TSS levels decreased towards both the proximal and distal ends of the fruit, but more so towards the proximal end. The equatorial region of the fruit was chosen as representative of the fruit for near infrared assessment of TSS. The spectral window for model development was optimised at 695-1045 nm, and the data pre-treatment procedure was optimised to second-derivative absorbance without scatter correction. The 'global' modified partial least squares (MPLS) regression modelling procedure of WINISI (ver. 1.04) was found to be superior with respect to root mean squared error of prediction (RMSEP) and bias for model predictions of TSS across seasons, compared with the 'local' MPLS regression procedure. Updating of the model with samples selected randomly from the independent validation population demonstrated improvement in both RMSEP and bias with addition of approximately 15 samples.

The sensitivity of updated NIR PLS models for fruit sorting to initial parameter settings

Partial least squares regression models on NIR spectra are often optimised (for wavelength range, mathematical pretreatment and outlier elimination) in terms of calibration terms of validation performance with reference to totally independent populations.

Predicting the response of wheat (Triticum aestivum L.) to liquid and granular phosphorus fertilisers in Australian soils

Liquid forms of phosphorus (P) have been shown to be more effective than granular P for promoting cereal growth in alkaline soils with high levels of free calcium carbonate on Eyre Peninsula, South Australia. However, the advantage of liquid over granular P forms of fertiliser has not been fully investigated across the wide range of soils used for grain production in Australia. A glasshouse pot experiment tested if liquid P fertilisers were more effective for growing spring wheat (Triticum aestivum L.) than granular P (monoammonium phosphate) in 28 soils from all over Australia with soil pH (H2O) ranging from 5.2 to 8.9. Application of liquid P resulted in greater shoot biomass, as measured after 4 weeks' growth (mid to late tillering, Feeks growth stage 2-3), than granular P in 3 of the acidic to neutral soils and in 3 alkaline soils. Shoot dry matter responses of spring wheat to applied liquid or granular P were related to soil properties to determine if any of the properties predicted superior yield responses to liquid P. The calcium carbonate content of soil was the only soil property that significantly contributed to predicting when liquid P was more effective than granular P. Five soil P test procedures (Bray, Colwell, resin, isotopically exchangeable P, and diffusive gradients in thin films (DGT)) were assessed to determine their ability to measure soil test P on subsamples of soil collected before the experiment started. These soil test values were then related to the dry matter shoot yields to assess their ability to predict wheat yield responses to P applied as liquid or granular P. All 5 soil test procedures provided a reasonable prediction of dry matter responses to applied P as either liquid or granular P, with the resin P test having a slightly greater predictive capacity on the range of soils tested. The findings of this investigation suggest that liquid P fertilisers do have some potential applications in non-calcareous soils and confirm current recommendations for use of liquid P fertiliser to grow cereal crops in highly calcareous soils. Soil P testing procedures require local calibration for response to the P source that is going to be used to amend P deficiency.

Development of near infrared analysis of faeces to estimate non-grass proportions in diets selected by cattle grazing tropical pastures

Grass (monocots) and non-grass (dicots) proportions in ruminant diets are important nutritionally because the non-grasses are usually higher in nutritive value, particularly protein, than the grasses, especially in tropical pastures. For ruminants grazing tropical pastures where the grasses are C-4 species and most non-grasses are C-3 species, the ratio of C-13/C-12 in diet and faeces, measured as delta C-13 parts per thousand, is proportional to dietary non-grass%. This paper describes the development of a faecal near infrared (NIR) spectroscopy calibration equation for predicting faecal delta C-13 from which dietary grass and non-grass proportions can be calculated. Calibration development used cattle faeces derived from diets containing only C-3 non-grass and C-4 grass components, and a series of expansion and validation steps was employed to develop robustness and predictive reliability. The final calibration equation contained 1637 samples and faecal delta C-13 range (parts per thousand) of [12.27]-[27.65]. Calibration statistics were: standard error of calibration (SEC) of 0.78, standard error of cross-validation (SECV) of 0.80, standard deviation (SD) of reference values of 3.11 and R-2 of 0.94. Validation statistics for the final calibration equation applied to 60 samples were: standard error of prediction (SEP) of 0.87, bias of -0.15, R-2 of 0.92 and RPD of 3.16. The calibration equation was also tested on faeces from diets containing C-4 non-grass species or temperate C-3 grass species. Faecal delta C-13 predictions indicated that the spectral basis of the calibration was not related to C-13/C-12 ratios per se but to consistent differences between grasses and non-grasses in chemical composition and that the differences were modified by photosynthetic pathway. Thus, although the calibration equation could not be used to make valid faecal delta C-13 predictions when the diet contained either C-3 grass or C-4 non-grass, it could be used to make useful estimates of dietary non-grass proportions. It could also be ut :sed to make useful estimates of non-grass in mixed C-3 grass/non-grass diets by applying a modified formula to calculate non-grass from predicted faecal delta C-13. The development of a robust faecal-NIR calibration equation for estimating non-grass proportions in the diets of grazing cattle demonstrated a novel and useful application of NIR spectroscopy in agriculture.

Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI).

Varying the spatial distribution of applied nitrogen (N) fertilizer to match demand in crops has been shown to increase profits in Australia. Better matching the timing of N inputs to plant requirements has been shown to improve nitrogen use efficiency and crop yields and could reduce nitrous oxide emissions from broad acre grains. Farmers in the wheat production area of south eastern Australia are increasingly splitting N application with the second timing applied at stem elongation (Zadoks 30). Spectral indices have shown the ability to detect crop canopy N status but a robust method using a consistent calibration that functions across seasons has been lacking. One spectral index, the canopy chlorophyll content index (CCCI) designed to detect canopy N using three wavebands along the "red edge" of the spectrum was combined with the canopy nitrogen index (CNI), which was developed to normalize for crop biomass and correct for the N dilution effect of crop canopies. The CCCI-CNI index approach was applied to a 3-year study to develop a single calibration derived from a wheat crop sown in research plots near Horsham, Victoria, Australia. The index was able to predict canopy N (g m-2) from Zadoks 14-37 with an r2 of 0.97 and RMSE of 0.65 g N m-2 when dry weight biomass by area was also considered. We suggest that measures of N estimated from remote methods use N per unit area as the metric and that reference directly to canopy %N is not an appropriate method for estimating plant concentration without first accounting for the N dilution effect. This approach provides a link to crop development rather than creating a purely numerical relationship. The sole biophysical input, biomass, is challenging to quantify robustly via spectral methods. Combining remote sensing with crop modelling could provide a robust method for estimating biomass and therefore a method to estimate canopy N remotely. Future research will explore this and the use of active and passive sensor technologies for use in precision farming for targeted N management.

Runoff, soil loss, and nutrient transport from cropping systems on Red Ferrosols in tropical northern Australia.

Runoff, soil loss, and nutrient loss were assessed on a Red Ferrosol in tropical Australia over 3 years. The experiment was conducted using bounded, 100-m(2) field plots cropped to peanuts, maize, or grass. A bare plot, without cover or crop, was also instigated as an extreme treatment. Results showed the importance of cover in reducing runoff, soil loss, and nutrient loss from these soils. Runoff ranged from 13% of incident rainfall for the conventional cultivation to 29% under bare conditions during the highest rainfall year, and was well correlated with event rainfall and rainfall energy. Soil loss ranged from 30 t/ha. year under bare conditions to <6 t/ha. year under cropping. Nutrient losses of 35 kg N and 35 kg P/ha. year under bare conditions and 17 kg N and 11 kg P/ha. year under cropping were measured. Soil carbon analyses showed a relationship with treatment runoff, suggesting that soil properties influenced the rainfall runoff response. The cropping systems model PERFECT was calibrated using runoff, soil loss, and soil water data. Runoff and soil loss showed good agreement with observed data in the calibration, and soil water and yield had reasonable agreement. Longterm runs using historical weather data showed the episodic nature of runoff and soil loss events in this region and emphasise the need to manage land using protective measures such as conservation cropping practices. Farmers involved in related, action-learning activities wished to incorporate conservation cropping findings into their systems but also needed clear production benefits to hasten practice change.

Wood properties and processing outcomes for plantation grown African mahogany (Khaya senegalensis) trees from Clare, Queensland (18 and 20 year-old trees) and Katherine, Northern Territory (14 year-old trees).

Khaya senegalensis, African mahogany, a high-value hardwood, was introduced in the Northern Territory (NT) in the 1950s; included in various trials there and at Weipa, Q in the 1960s-1970s; planted on ex mine sites at Weipa (160 ha) until 1985; revived in farm plantings in Queensland and in trials in the NT in the 1990s; adopted for large-scale, annual planting in the Douglas-Daly region, NT from 2006 and is to have the planted area in the NT extended to at least 20,000 ha. The recent serious interest from plantation growers, including Forest Enterprises Australia Ltd (FEA), has seen the establishment of some large scale commercial plantations. FEA initiated the current study to process relatively young plantation stands from both Northern Territory and Queensland plantations to investigate the sawn wood and veneer recovery and quality from trees ranging from 14 years (NT – 36 trees) to 18-20 years (North Queensland – 31 trees). Field measures of tree size and straightness were complemented with log end splitting assessment and cross-sectional disc sample collection for laboratory wood properties measurements including colour and shrinkage. End-splitting scores assessed on sawn logs were relatively low compared to fast grown plantation eucalypts and did not impact processing negatively. Heartwood proportion in individual trees ranged from 50% up to 92 % of butt cross-sectional disc area for the visually-assessed dark coloured central heartwood and lighter coloured transition wood combined. Dark central heartwood proportion was positively related to tree size (R2 = 0.57). Chemical tests failed to assist in determining heartwood – sapwood boundary. Mean basic density of whole disc samples was 658 kg/m3 and ranged among trees from 603 to 712 kg/m3. When freshly sawn, the heartwood of African mahogany was orange-red to red. Transition wood appeared to be pinkish and the sapwood was a pale yellow colour. Once air dried the heartwood colour generally darkens to pinkish-brown or orange-brown and the effect of prolonged time and sun exposure is to darken and change the heartwood to a red-brown colour. A portable colour measurement spectrophotometer was used to objectively assess colour variation in CIE L*, a* and b* values over time with drying and exposure to sunlight. Capacity to predict standard colour values accurately after varying periods of direct sunlight exposure using results obtained on initial air-dried surfaces decreased with increasing time to sun exposure. The predictions are more accurate for L* values which represent brightness than for variation in the a* values (red spectrum). Selection of superior breeding trees for colour is likely to be based on dried samples exposed to sunlight to reliably highlight wood colour differences. A generally low ratio between tangential and radial shrinkages was found, which was reflected in a low incidence of board distortion (particularly cupping) during drying. A preliminary experiment was carried out to investigate the quality of NIR models to predict shrinkage and density. NIR spectra correlated reasonably well with radial shrinkage and air dried density. When calibration models were applied to their validation sets, radial shrinkage was predicted to an accuracy of 76% with Standard Error of Prediction of 0.21%. There was also a strong predictive power for wood density. These are encouraging results suggesting that NIR spectroscopy has good potential to be used as a non-destructive method to predict shrinkage and wood density using 12mm diameter increment core samples. Average green off saw recovery was 49.5% (range 40 to 69%) for Burdekin Agricultural College (BAC) logs and 41.9% (range 20 to 61%) for Katherine (NT) logs. These figures are about 10% higher than compared to 30-year-old Khaya study by Armstrong et al. (2007) however they are inflated as the green boards were not docked to remove wane prior to being tallied. Of the recovered sawn, dried and dressed volume from the BAC logs, based on the cambial face of boards, 27% could potentially be used for select grade, 40% for medium feature grade and 26% for high feature grades. The heart faces had a slightly higher recovery of select (30%) and medium feature (43%) grade boards with a reduction in the volume of high feature (22%) and reject (6%) grade boards. Distribution of board grades for the NT site aged 14 years followed very similar trends to those of the BAC site boards with an average (between facial and cambial face) 27% could potentially be used for select grade, 42% for medium feature grade, 26% for high feature grade and 5% reject. Relatively to some other subtropical eucalypts, there was a low incidence of borer attack. The major grade limiting defects for both medium and high feature grade boards recovered from the BAC site were knots and wane. The presence of large knots may reflect both management practices and the nature of the genetic material at the site. This stand was not managed for timber production with a very late pruning implemented at about age 12 years. The large amount of wane affected boards is indicative of logs with a large taper and the presence of significant sweep. Wane, knots and skip were the major grade limiting defects for the NT site reflecting considerable amounts of sweep with large taper as might be expected in younger trees. The green veneer recovered from billets of seven Khaya trees rotary peeled on a spindleless lathe produced a recovery of 83% of green billet volume. Dried veneer recovery ranged from 40 to 74 % per billet with an average of 64%. All of the recovered grades were suitable for use in structural ply in accordance to AS/NZ 2269: 2008. The majority of veneer sheets recovered from all billets was C grade (27%) with 20% making D grade and 13% B grade. Total dry sliced veneer recovery from the logs of the two largest logs from each location was estimated to be 41.1%. Very positive results have been recorded in this small scale study. The amount of colour development observed and the very reasonable recoveries of both sawn and veneer products, with a good representation of higher grades in the product distribution, is encouraging. The prospects for significant improvement in these results from well managed and productive stands grown for high quality timber should be high. Additionally, the study has shown the utility of non-destructive evaluation techniques for use in tree improvement programs to improve the quality of future plantations. A few trees combined several of the traits desired of individuals for a first breeding population. Fortunately, the two most promising trees (32, 19) had already been selected for breeding on external traits, and grafts of them are established in the seed orchard.

Simultaneous prediction of acidity parameters (pH and titratable acidity) in Kefir using near infrared reflectance spectroscopy.

Acidity in terms of pH and titratable acids influences the texture and flavour of fermented dairy products, such as Kefir. However, the methods for determining pH and titratable acidity (TA) are time consuming. Near infrared (NIR) spectroscopy is a non-destructive method, which simultaneously predicts multiple traits from a single scan and can be used to predict pH and TA. The best pH NIR calibration model was obtained with no spectral pre-treatment applied, whereas smoothing was found to be the best pre-treatment to develop the TA calibration model. Using cross-validation, the prediction results were found acceptable for both pH and TA. With external validation, similar results were found for pH and TA, and both models were found to be acceptable for screening purposes.

Finding genes for economically important traits: Brahman cattle puberty.

Age at puberty is an important component of reproductive performance in beef cattle production systems. Brahman cattle are typically late-pubertal relative to Bos taurus cattle and so it is of economic relevance to select for early age at puberty. To assist selection and elucidate the genes underlying puberty, we performed a genome-wide association study (GWAS) using the BovineSNP50 chip (similar to 54 000 polymorphisms) in Brahman bulls (n = 1105) and heifers (n = 843) and where the heifers were previously analysed in a different study. In a new attempt to generate unbiased estimates of single-nucleotide polymorphism (SNP) effects and proportion of variance explained by each SNP, the available data were halved on the basis of year and month of birth into a calibration and validation set. The traits that defined age at puberty were, in heifers, the age at which the first corpus luteum was detected (AGECL, h(2) = 0.56 +/- 0.11) and in bulls, the age at a scrotal circumference of 26 cm (AGE26, h(2) = 0.78 +/- 0.10). At puberty, heifers were on average older (751 +/- 142 days) than bulls (555 +/- 101 days), but AGECL and AGE26 were genetically correlated (r = 0.20 +/- 0.10). There were 134 SNPs associated with AGECL and 146 SNPs associated with AGE26 (P < 0.0001). From these SNPs, 32 (similar to 22%) were associated (P < 0.0001) with both traits. These top 32 SNPs were all located on Chromosome BTA 14, between 21.95 Mb and 28.4 Mb. These results suggest that the genes located in that region of BTA 14 play a role in pubertal development in Brahman cattle. There are many annotated genes underlying this region of BTA 14 and these are the subject of current research. Further, we identified a region on Chromosome X where markers were associated (P < 1.00E-8) with AGE26, but not with AGECL. Information about specific genes and markers add value to our understanding of puberty and potentially contribute to genomic selection. Therefore, identifying these genes contributing to genetic variation in AGECL and AGE26 can assist with the selection for early onset of puberty.

Effects of seasonal variability on FT-NIR prediction of dry matter content for whole Hass avocado fruit

Fourier Transform (FT)-near infra-red spectroscopy (NIRS) was investigated as a non-invasive technique for estimating percentage (%) dry matter of whole intact 'Hass' avocado fruit. Partial least squares (PLS) calibration models were developed from the diffuse reflectance spectra to predict % dry matter, taking into account effects of seasonal variation. It is found that seasonal variability has a significant effect on model predictive performance for dry matter in avocados. The robustness of the calibration model, which in general limits the application for the technique, was found to increase across years (seasons) when more seasonal variability was included in the calibration set. The R-v(2) and RMSEP for the single season prediction models predicting on an independent season ranged from 0.09 to 0.61 and 2.63 to 5.00, respectively, while for the two season models predicting on the third independent season, they ranged from 0.34 to 0.79 and 2.18 to 2.50, respectively. The bias for single season models predicting an independent season was as high as 4.429 but <= 1.417 for the two season combined models. The calibration model encompassing fruit from three consecutive years yielded predictive statistics of R-v(2) = 0.89, RMSEP = 1.43% dry matter with a bias of -0.021 in the range 16.1-39.7% dry matter for the validation population encompassing independent fruit from the three consecutive years. Relevant spectral information for all calibration models was obtained primarily from oil, carbohydrate and water absorbance bands clustered in the 890-980, 1005-1050, 1330-1380 and 1700-1790 nm regions. These results indicate the potential of FT-NIRS, in diffuse reflectance mode, to non-invasively predict the % dry matter of whole 'Hass' avocado fruit and the importance of the development of a calibration model that incorporates seasonal variation. Crown Copyright (c) 2012 Published by Elsevier B.V. All rights reserved.

Classifying genotype by environment interactions for targeted germplasm deployment with a focus on Eucalyptus

A novel methodology for describing genotype by environment interactions estimated from multi-environment field trials is described and an empirical example using an extensive trial network of eucalypts is presented. The network of experiments containing 65 eucalypts was established in 38 replicated field trials across the tropics and subtropics of eastern Australia, with a selection of well-tested species used to provide a more detailed examination of productivity differentials across environmental gradients. By focusing on changes in species’ productivity across environmental gradients, the results are applicable for all species established across the range of environments evaluated in the trial network and simultaneously classify species and environments so that results may be applied across the landscape. The methodology developed was able to explain most (93 %) of the variation in the selected species relative changes in productivity across the various environmental variables examined. Responses were primarily regulated by changes in variables related to water availability and secondarily by temperature related variables. Clustering and ordination can identify groups of species with similar physiological responses to environment and may also guide the parameterisation and calibration of process based models of plant growth. Ordination was particularly useful in the identification of species with distinct environmental response patterns that would be useful as probes for extracting more information from future trials.

Estimating Hydrogen Cyanide in Forage Sorghum (Sorghum bicolor) by Near-Infrared Spectroscopy

Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals when grazing forage sorghum. Developing technologies to monitor the level of HCN in the growing crop would benefit graziers, so that they can move cattle into paddocks with acceptable levels of HCN. In this study, we developed near-infrared spectroscopy (MRS) calibrations to estimate HCN in forage sorghum and hay. The full spectral NIRS range (400-2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e., visible (400-750 nm), shortwave (800-1100 nm) and near-infrared (NIR) (1100-2498 nm). Using the full spectrum approach and partial least-squares (PLS), the calibration produced a coefficient of determination (R-2) = 0.838 and standard error of cross-validation (SECV) = 0.040%, while the validation set had a R-2 = 0.824 with a low standard error of prediction (SEP = 0.047%). When using a multiple linear regression (MLR) approach, the best model (NIR spectra) produced a R-2 = 0.847 and standard error of calibration (SEC) = 0.050% and a R-2 = 0.829 and SEP = 0.057% for the validation set. The MLR models built from these spectral regions all used nine wavelengths. Two specific wavelengths 2034 and 2458 nm were of interest, with the former associated with C=O carbonyl stretch and the latter associated with C-N-C stretching. The most accurate PLS and MLR models produced a ratio of standard error of prediction to standard deviation of 3.4 and 3.0, respectively, suggesting that the calibrations could be used for screening breeding material. The results indicated that it should be feasible to develop calibrations using PLS or MLR models for a number of users, including breeding programs to screen for genotypes with low HCN, as well as graziers to monitor crop status to help with grazing efficiency.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Evaluating and validating abundance monitoring methods in the absence of populations of known size: review and application to a passive tracking index

Rarely is it possible to obtain absolute numbers in free-ranging populations and although various direct and indirect methods are used to estimate abundance, few are validated against populations of known size. In this paper, we apply grounding, calibration and verification methods, used to validate mathematical models, to methods of estimating relative abundance. To illustrate how this might be done, we consider and evaluate the widely applied passive tracking index (PTI) methodology. Using published data, we examine the rationality of PTI methodology, how conceptually animal activity and abundance are related and how alternative methods are subject to similar biases or produce similar abundance estimates and trends. We then attune the method against populations representing a range of densities likely to be encountered in the field. Finally, we compare PTI trends against a prediction that adjacent populations of the same species will have similar abundance values and trends in activity. We show that while PTI abundance estimates are subject to environmental and behavioural stochasticity peculiar to each species, the PTI method and associated variance estimate showed high probability of detection, high precision of abundance values and, generally, low variability between surveys, and suggest that the PTI method applied using this procedure and for these species provides a sensitive and credible index of abundance. This same or similar validation approach can and should be applied to alternative relative abundance methods in order to demonstrate their credibility and justify their use.

Exploring the Potential Impacts of Climate Variability on Spring Wheat Yield with the APSIM Decision Support Tool

Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential changes in spring wheat yields at Swift Current and Melfort, Canada, for different sowing windows under projected climate scenarios (i.e., the representative concentration pathways, RCP4.5 and RCP8.5). First, the APSIM model was calibrated and evaluated at the study sites using data from long term experimental field plots. Then, the impacts of change in sowing dates on final yield were assessed over the 2030-2099 period with a 1990-2009 baseline period of observed yield data, assuming that other crop management practices remained unchanged. Results showed that the performance of APSIM was quite satisfactory with an index of agreement of 0.80, R2 of 0.54, and mean absolute error (MAE) and root mean square error (RMSE) of 529 kg/ha and 1023 kg/ha, respectively (MAE = 476 kg/ha and RMSE = 684 kg/ha in calibration phase). Under the projected climate conditions, a general trend in yield loss was observed regardless of the sowing window, with a range from -24 to -94 depending on the site and the RCP, and noticeable losses during the 2060s and beyond (increasing CO2 effects being excluded). Smallest yield losses obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate suggested that this option might be explored for mitigating possible adverse impacts of climate variability. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under potential climate variability within Western Canada.