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ABSTRACTFor decades researches have been conducted in Ethiopia to improve wheat productivity and quality. This involved extensive selection of breeding lines based on agronomic and laboratory data from numerous trials. However, generating laboratory data for all these services was both time-consuming and expensive. Hence, the study aimed to develop a very rapid and cost-effective testing method for wheat genotypes and varieties using near-infrared spectroscopy. For this purpose, we collected 155 bread wheat samples from high-growing potential areas of Sinana, Kulumsa, and Holeta. The wet chemistry analysis was done in duplicate following the international standard official methods. The same samples were also scanned using TANGO’S Bruker NIRS machine in duplicate to acquire spectral data. A calibration model was then developed by fitting the reference and spectral data using the partial least square (PLS). The calibration models exhibited a high level of strength for all parameters. The performance of the calibration model for protein (R2 = 0.99; RPD = 9.63), moisture (R2 = 0.96; RPD = 5.29), ash (R2 = 0.98; RPD = 6.39), total gluten (R2 = 0.98; RPD = 8.09), dry gluten (R2 = 0.97; RPD = 5.57), gluten index (R2 = 0.96; RPD = 5.23), and falling number (R2 = 0.97 and RPD = 6.23) were deemed suitable for wheat process and quality control purposes. But the models for moisture and gluten index (R2 = 0.96) were found to be suitable for screening and simple quality control purposes only. Therefore, this study clearly demonstrated that the near-infrared spectroscopy model is a highly reliable and robust method for predicting the key quality traits of bread wheat grains, serving the needs of both breeding programs and industry raw material control.

The mechanical properties of wheat grains were measured and analyzed using discrete element software, which provided crucial data for their processing in a mill. The foundational Hertz–Mindlin model was used as a theoretical framework to evaluate the accumulation angle of wheat grains. The Plackett–Burman, steepest ascent, and Box–Behnken methods were utilized in a series of experiments, with the accumulation angle serving as the dependent variable. Targeting the actual angle of repose in the response surface, the optimal parameters were derived using the regression equations. These included a static‐friction coefficient of 0.3 between individual wheat grains, rolling‐friction coefficient of 0.04 for wheat–wheat interactions, static‐friction coefficient of 0.554 for wheat–tooth roller interactions, collision recovery coefficient of 0.5 for wheat–wheat collisions, collision recovery coefficient of 0.45 for wheat–tooth roller collisions, and rolling‐friction coefficient of 0.05 for wheat–roller interactions. Relying on the bonding contact model of Hertz–Mindlin, virtual uniaxial compression tests were conducted to calibrate the wheat grain bonding parameters. A regression equation for the critical load was subsequently generated using the critical load of the wheat grain bonding model as the response variable. The optimal parameters were calculated and incorporated into the EDEM model for computation, which resulted in a relative error of 1.6% between the calculated and observed values, confirming the accuracy and feasibility of the calibration method, suggesting that the calibrated parameters were accurate. [ABSTRACT FROM AUTHOR]

Functional–structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost, and, as a result, error propagation is usually ignored. Here we applied an automatic method to the calibration of WALTer: a functional–structural wheat model that simulates the plasticity of tillering in response to competition for light. We used a Bayesian calibration method to jointly estimate the values of five parameters and quantify their uncertainty by fitting the model outputs to tillering dynamics data. We made recourse to Gaussian process metamodels in order to alleviate the computational cost of WALTer. These metamodels are built from an adaptive design that consists of successive runs of WALTer chosen by an efficient global optimization algorithm specifically adapted to this particular calibration task. The method presented here performed well on both synthetic and experimental data. It is an efficient approach for the calibration of WALTer and should be of interest for the calibration of other functional–structural plant models. [ABSTRACT FROM AUTHOR]

The self-sufficiency ratio and national average yield of wheat are low in Japan. Reducing the yield gap and receiving the government subsidy for grain quality are vital strategies for profitability. Elucidating optimum nitrogen application scheme is awaited to attain both higher yield and appropriate grain protein content (GPC) for wheat cultivation in Japan. Such decision support can be realized by integrating field experimental knowledge to crop growth models, although they have scarcely been utilized for wheat production in Japan. Therefore, the purposes of this study were to apply a widely used crop growth model (APSIM) to wheat growth on an Andosol in the Kanto region in Japan by calibration and validation. Selected model parameters of APSIM-wheat for phenology, leaf growth, and grain formation were readjusted based on the phenology and growth data of soft and hard wheat cultivars. Then the model was validated by using similar variables obtained in an independent experiment. For the simulation of the optimum sowing for winter wheat in the Kanto area (November), the root mean square error for grain yield was 23 and 48 g m−2 for Ayahikari and Yumeshiho varieties, respectively, and that for GPC was 1.9 and 1.4%. Thus, the overall model performance was acceptable for optimum sowing. However, grain yield and dry matter production were significantly overestimated when the data of late sowing groups were included. Therefore, further model improvement was suggested to add an algorithm to reduce the number of emerged plants under cold temperature in late sowing conditions.

This study was carried out to determine the optimum levels of NO3-N (nitrate – nitrogen) in soils. According to the randomized complete block design, increased N applications (0, 40, 80, 120, 160, 200 and 240 kg N ha−1) were applied as urea (46% N) in 10 locations with different amounts of nitrate-nitrogen in the soil profile (0–100 cm). As a result, statistically significant relationships were determined between the maximum product increase in wheat and NO3-N amount determined by the phenoldisulfonic acid method at 0–20, 0–40, 0–60, 0–80 and 0–100 cm depths of the trial field soils. Considering these relationships, Mitscherlich–Bray equation was expressed as [log (A-y) = log A – 0.048*b – 0.007*x] for the bread wheat plants grown in the calcareous soils under irrigated conditions. The levels of NO3-N in the soil and consequently the amount of nitrogen fertilizer to be applied in calcareous soils varies according to the economically acceptable rate of maximum yield of bread wheat. Hence, the values of NO3-N determined at 0–20 cm depth was categorized as insufficient (<20 kg ha−1), sufficient (20.1 to 34 kg ha−1) and excessive (>34 kg ha−1). [ABSTRACT FROM AUTHOR]

The self-sufficiency ratio and national average yield of wheat are low in Japan. Reducing the yield gap and receiving the government subsidy for grain quality are vital strategies for profitability. Elucidating optimum nitrogen application scheme is awaited to attain both higher yield and appropriate grain protein content (GPC) for wheat cultivation in Japan. Such decision support can be realized by integrating field experimental knowledge to crop growth models, although they have scarcely been utilized for wheat production in Japan. Therefore, the purposes of this study were to apply a widely used crop growth model (APSIM) to wheat growth on an Andosol in the Kanto region in Japan by calibration and validation. Selected model parameters of APSIM-wheat for phenology, leaf growth, and grain formation were readjusted based on the phenology and growth data of soft and hard wheat cultivars. Then the model was validated by using similar variables obtained in an independent experiment. For the simulation of the optimum sowing for winter wheat in the Kanto area (November), the root mean square error for grain yield was 23 and 48 g m−2 for Ayahikari and Yumeshiho varieties, respectively, and that for GPC was 1.9 and 1.4%. Thus, the overall model performance was acceptable for optimum sowing. However, grain yield and dry matter production were significantly overestimated when the data of late sowing groups were included. Therefore, further model improvement was suggested to add an algorithm to reduce the number of emerged plants under cold temperature in late sowing conditions. [ABSTRACT FROM AUTHOR]

A comparison was made between the recently used crop models (DSSAT-CERES and AquaCrop), based on high accuracy, to simulate wheat growth under different water and nitrogen levels. Hence, four irrigation treatments (rainfed, irrigate at 50% and 75% of field capacity (FC), and irrigate at 100% of FC) were considered as the main factor with three nitrogen fertilizer levels as the sub-main factor (viz. non-fertilizer, 100 kg N ha−1, and 200 kg N ha−1) during 2014–2016. Model efficiency (E), Wilmott's index of agreement (d), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE) were used to test the model performance. AquaCrop model was calibrated to simulate the grain and biomass yield with the prediction error statistics of 0.87 < E < 0.90, 0.24 < RMSE < 0.36 t ha−1, 6 < NRMSE < 7.2%, and 0.90 < d < 0.93, respectively. Subsequently, the CERES model was calibrated and the performance was in line (0.88 < E < 0.93, 0.92 < d < 0.96, 0.19 < RMSE < 0.34 t ha−1, and 5.7 < NRMSE < 5.8%), respectively. Upon validation, E was 0.89 and 0.91; d was 0.93 and 0.96, RMSE was 0.25 and 0.21 t ha−1, and NRMSE was 6.4 and 5.9 for grain yield by AquCrop and CERES models for all treatments, respectively. Overall, the simulation results by the DSSAT model were relatively more accurate than the AquaCrop model. But, considering the low required input data, the AquaCrop model is recommended for wheat yield simulation in the conditions of unavailability of equipment and crop data. [ABSTRACT FROM AUTHOR]

The discrete element method (DEM) parameters of wheat were calibrated for the modelling of a grain storage system. In addition, the effects of material parameters on the accuracy of DEM modelling of the oedometric compaction and unloading of bulk wheat were analysed. The bi-stiffness Hertz-like contact model was applied to incorporate the hysteretic behaviour of individual grains. The parameters of the contact model were calibrated based on experimental data of the loading–unloading cycle of a single wheat grain. The results of DEM simulations of the bulk behaviour of grain were validated using experimental data from oedometric tests. The observed trends indicate good correspondence between the experimental data and DEM simulations using the calibrated parameters. Simulations performed assuming spherical particles provide the best approximation of the loading–unloading cycle. The application of a special procedure for performing DEM simulations allows for the accurate reproduction of experimental values of the initial, compacted, and unloaded porosities of bulk wheat grains in the considered moisture-content range considered. Image 1 • Bi-stiffness contact model is positively verified for the storage of wheat. • Calibration of the DEM parameters of wheat for storage is performed. • Bulk properties of wheat grain are modelled and verified experimentally. • The effect of moisture content on bulk properties is examined. [ABSTRACT FROM AUTHOR]

The land surface characteristic influences climatic environments by controlling the land surface energy and water fluxes. Although large progresses have been made in coupling detailed crop models into land surface models, there is much room left for improved estimates of surface fluxes in double cropping system, such as the winter wheat (Triticum aestivum L.)–summer maize (Zea mays L.) system under intensive influences of climate change and human activities in the North China Plain (NCP). Here, we modeled the growth and surface fluxes in winter wheat–summer maize double cropping system in the NCP through modified SiBcrop model. The parameters related with the crop growth in winter wheat and maize sub-models were emphasized to better simulate the seasonal dynamics of phenology phase, leaf area index (LAI), and latent (LH) and sensible (SH) heat fluxes. Observational data for phenology, LAI, and energy fluxes were compiled from Yucheng and Guantao stations, to calibrate and validate the SiBcrop model. The results showed that the adjusted SiBcrop captured better the seasonal dynamics of phenology, LAI, LH, and SH, relative to the original code. The largest biases in model results were in SH when horizontal advection prevails and in LH when there is summer maize senescence. Sensitivity analysis illustrated that growth duration, emergence date, LH, SH, and Bowen ratio were quite sensitive to sowing date of wheat, and growth duration and emergence and harvest dates to sowing of maize. The calibrated SiBcrop also simulated the LH and SH well at Guantao station. The adjusted SiBcrop is capable of assessing the responses of surface biophysical processes to the changes of human management and climate change in the double cropping system in the NCP. [ABSTRACT FROM AUTHOR]

Critical ranges for soil tests are based on results that inevitably involve some broad variance around the fitted relationship. Some of the variation is related to field-based factors affecting crop response to nutrients in the soil and some to the efficiency of the soil-test extractant itself. Most attempts to improve soil tests focus on the extractant, whereas here, we explore the variation that could be accounted for by field-based factors in the soil-test calibration relationship between Colwell phosphorus (P) and wheat yield, using the Australian Better Fertiliser Decisions for Crops database—the biggest dataset available for this relationship. Calibrations developed from this dataset have been criticised, and so we aimed to explore factors accounting for more of the variation in the relationships for the dryland, winter-dominant rainfall region of southern New South Wales. As reported previously, soil type was shown to influence the critical range and r -value for the Colwell P soil-test calibration for P responses by wheat. We also identified a tendency for dry conditions, at sowing or during the season, to lower relative yields for a given soil-test value, indicating increased reliance on fertiliser P over soil P. A similar trend was evident for later sowing date, again suggesting an increased probability of crop P requirements being met from the fertiliser P. However, additional records need to be generated to establish definitively that early sowing or subsurface P reserves minimise response to fertiliser P. In general, factors that influence crop access to soil P will have an impact on response to fertiliser P. Although this analysis shows that it is possible to 'tighten' the response curve for Colwell P and wheat by restricting the data for a given soil type to ideal management and seasonal conditions, the 'outliers' that are excluded frequently reflect an important subset of environmental conditions encountered by wheat crops in dryland agriculture. Soil testing is used to guide fertiliser requirements for crops, but the calibration of soil tests with crop yield can be imprecise. We present evidence that some of the variation in the soil-test calibration is due to soil, environmental and management factors. Understanding this variation in soil test calibration enables recommendations to be made that are more relevant to the conditions faced by farmers, such as for late sowings in dry years. [ABSTRACT FROM AUTHOR]

Two dynamic simulation models viz. cERES-Wheat and Infocrop-Wheat model were used to estimate the yield and phenology of wheat. For the calibration of the models, the data on three cultivars of wheat sown under five dates of sowing during Rabi 2014-15 was used. The models were validated using similar data sets from experiments conducted during Rabi 2015-16. The model overestimated/ underestimated the phenology and yield of wheat. The deviation in the days taken to anthesis and physiological maturity was -9 to +3 and -7 to +1 day, respectively by cERES-Wheat and -3 to +11 and -10 to +1 days, respectively by Infocrop-Wheat model. The d-stat values for the days taken to anthesis, physiological maturity and grain yield in cERES-Wheat model were 0.82, 0.60 and 0.85%, respectively whereas in Infocrop-Wheat model the respective values were 0.87, 0.81 and 0.80. The RMSE values for the days taken to anthesis, physiological maturity and grain yield were 3.48 days, 3.87 days and 493.8 kg/ha, respectively in cERES-Wheat model and 7.83 days, 6.88 days and 434.4 kg/ha, respectively in Infocrop- Wheat model. The results of the study indicate that the models were well calibrated and validated for further use to predict phenology and yield of wheat in Punjab. [ABSTRACT FROM AUTHOR]

• RDT sensors continuously measure elongation rates through a rotational pulley system. • RDT sensors can be accurately calibrated. • The RDT – pulley system is robust against environmental and physical disturbances. • RDT sensors can successfully measure leaf growth and the effect of drought stress. • Growth of supporting structures must be considered in monocots when interpreting data. Flag leaf growth has been emphasized in the literature as an important secondary trait for wheat breeding under drought stress. To measure leaf elongation rate (LER) in monocotyledons such as wheat, the rotational displacement transducer (RDT) has already been used in several studies, mostly on maize. Still, a comprehensive calibration and measurement protocol of the sensor is lacking. To fill this gap, several experiments were performed: (i) to calibrate the sensor and test its resilience to physical disturbances and changes in environmental conditions, (ii) to validate the calibration on growing plants, and (iii) to compare growth rate in flag leaves of well-watered and drought-treated wheat (Triticum aestivum L.) plants. The study showed that calibration of RDT sensors with a height gauge resulted in very accurate and robust measurements of growth rate and drought stress dynamics in monocotyledons, such as wheat. To correctly interpret the sensor measurements and derive the underlying mechanism, it is however important to consider the complex architecture of the plant, as the RDT not merely measures leaf growth, but also any potential growth of supporting parts. [ABSTRACT FROM AUTHOR]

Sensor-based approaches to delivering nitrogen (N) fertilizer recommendations often rely on field experimentation to build calibrations between sensor readings and particular crop variables of interest. One important question is the extent to which calibrations derived from specific locations in specific seasons can be confidently extrapolated to other locations in future seasons. In this study, we examined a dataset covering 19 yr of wheat (Triticum aestivum L.) yield potential and crop N response calibrations to help answer two questions. First, what drives the variability in sensor calibration between different years? Second, what are the consequences of seasonal variability for the accuracy of sensor predictions and N recommendations? Deep soil moisture information, measured between 50 and 90 d after sowing, helped to explain the variability in the R² and equation coefficients of yield potential and response index sensor calibrations between years. When data from multiple experimental years were combined to build generalized models, the R2 was quite low (-0.25 for all 19 yr of data). Consequently, the benefit of the N rates recommended by the sensor over a conventional application based on historical optimum N rates was insignificant. We suggest that, rather than increasing experimental years indiscriminately, the relevant seasonal covariates should be incorporated into the sensor predictions in order build holistic sensor models. The results also highlight the likelihood that relevant site-specific information, such as soil moisture status, may also explain between-site variation in sensor calibration and so promote an enhanced ability to extrapolate calibrations from one location to another. [ABSTRACT FROM AUTHOR]

Rigorous calibration of crop phenology models, providing both best-estimate parameters and estimates of parameter uncertainty, is essential for evaluating how crops will respond to future environmental and management changes. Least squares parameter estimation is a widely used approach to calibration of nonlinear models, and there are many software packages available for implementing this approach. However, these packages are rarely if ever used for complex phenology models because of technical difficulties. The purpose of this research is to overcome these difficulties, in particular the issue of a model which is a discontinuous function of the parameters. The calculations were conducted with the WheatGrow phenology model, but the approach is applicable to other complex phenology models. The approach was used to calibrate WheatGrow phenology for 4 widely used cultivars in the main winter wheat production region of China. The resulting fit to the data was quite good (root mean squared error (RMSE) of 3–4 days for flowering and maturity). The coefficients of variation (CV) of the parameters ranged from 6% to 40%. Furthermore, the model was used to predict the effect of warming on phenology, and the uncertainty in those predictions. The results showed that each degree of warming reduced the time from sowing to flowering by 7–8 days for the spring cultivars and 3–4 days for the winter cultivars. The time form flowering to maturity is hardly affected. In addition, the higher the temperature, the larger the uncertainty in the predictions. Comparison with variability in multi-model ensembles suggests that parameter uncertainty is less than the model uncertainty. [ABSTRACT FROM AUTHOR]

Trichothecenes are mycotoxins generated by Fusarium species that are toxic to humans and animals and frequently contaminate cereals. Herein, we present an UHPLC-Q-Orbitrap-HRMS method using one-step pretreatment for simultaneous detection of 16 type A and B trichothecenes in maize and wheat. A calibration strategy based on natural isotopic distribution was adopted and validated to replace traditional time-consuming and laborious multipoint external calibration curves. Various conditions influencing the method were studied. The recoveries of spiked analytes at three different concentrations ranged from 74.2% to 105.3% and the inter-day relative standard deviations were 0.5–15.6%. The limits of quantitation were 4.4–108.8 μg/kg. Moreover, the current methodology was applied to the evaluation of type A and B trichothecenes present in samples from major wheat production areas in China. This study provides a facile methodology for type A and B trichothecenes analysis and presents promising applications for exposure risk assessment of trichothecenes in cereals. [Display omitted] • An UHPLC-Q-Orbitrap-HRMS method was developed for 16 type A and B trichothecenes and their modified mycotoxins. • One-step pretreatment method coupled with a novel isotopic multipoint calibration method was adapted. • The new method was applied to systematically evaluate the type A and B trichothecenes in wheat in China. • DON-3G exhibited a significant linear correlation with DON in wheat. [ABSTRACT FROM AUTHOR]

The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software, to predict phenology and grain yield of durum wheat, common wheat, and maize in different Italian environments. A 10-year (2001−2010) dataset was used to optimize the genetic parameters for selected varieties of each species and to evaluate the models considering several statistical indexes. The generalized likelihood uncertainty estimation method, and trial and error approach were used to optimize the cultivar-specific parameters of these models. Results show good model performances in reproducing crop phenology and yield for the analyzed crops, especially with the parameters optimized with the trial and error procedure. Highly significant (p ≤ 0.001) correlations between observed and simulated data were found for both anthesis and yield in model calibration and evaluation (p ≤ 0.01 for grain yield of maize in model evaluation). Root mean square error (RMSE) values range from six to nine days for anthesis and from 1.1 to 1.7 t ha−1 for crop yield and index of agreement (d-index) from 0.96 to 0.98 for anthesis and from 0.8 to 0.87 for crop yield. The set of genetic parameters obtained for durum wheat, common wheat, and maize may be applied in further analyses at field, regional, and national scales to guide operational (farmers), strategic, and tactical (policy makers) decisions.

Sunn pest, Eurygaster integriceps, is a key pest of wheat and barley in Iran. At present, infested areas are treated with ground sprayers. A survey was conducted to evaluate the efficiency and calibration accuracy of ground hydraulic sprayers in Iran. The results showed that the motorized lance sprayer and manually operated sprayer were the most conventional sprayers and fenitrothion EC 50% and deltamethrin EC 2.5% were the conventional insecticides against sunn pest in Kermanshah province. The mean efficacy percentage was 88% and 35% against nymphs and overwintered adults, respectively. In motorized lance sprayers, there were 5% and 90% overdosage of fenitrothion and deltamethrin, respectively; while, in manually carried sprayers, there were 5% and 25% overdosage of fenitrothion and deltamethrin, respectively. Applied dosages were 5-20% and 25-90% more than recommended dosages of fenitrothion and deltamethrin, respectively. Errors in effectiveness and applied dosage were higher in motorized lance sprayer than manually carried sprayers. [ABSTRACT FROM AUTHOR]

Nondestructive and fast chlorophyll assessment can be made with portable chlorophyll meters. The meters measure the transmittance of red and near infrared radiation through the leaf and estimates the chlorophyll in non-dimensional index values by defined arithmetic operations.These metes required to calibrate for conversion of index values to the actual chlorophyll content in desired units. Calibration of widely used TYS-A (SPAD meter) and CCM-200 (Chlorophyll content index (CCI) meter) chlorophyll meters wasattempted for chlorophyll estimation in bread wheat (Triticum aestivum L.) leaves.The chlorophyll values for the wheat leaves were measured with chlorophyll meters and same samples were used in standard photometric measurements in laboratory. The SPAD values showed statistically significant linear relationship with chlorophyll content (R2=0.92). The functional relationships represented with regression models can be used to convert the SPAD value to actual chlorophyll content for wheat. The relation between SPAD and CCI was studied using independent leaf samples (n=159). The relationship was found curvilinear and fitted with statistically significant quadratic polynomial (R2=0.86).So, the fitted quadratic model can be used for inter-conversion between SPAD and CCI. The model was used to convert the values from SPAD to CCI. The derived CCI values were used to fit establish the relationship between CCI and actual chlorophyll content of wheat.

Crop growth in process based crop models is controlled by different parameters. Model calibration is necessary for application to new cultivars and environment. We applied a manual method to calibrate APSIM-Wheat and CERES-Wheat for the flowering day, maturity day, leaf area index, biomass and grain yield of five spring wheat cultivars under rainfed conditions in Pakistan. Five wheat cultivars of diverse origin namely Tatara, NARC-2009, Sehar-2006, SKD-1 and F-Sarhad were planted on 19th November, at Islamabad during the years 2007–2011. The experiments were laid out in Randomized Complete Block Design (RCBD) replicated four times with individual plot size of 5 m × 3 m. APSIM-Wheat and CERES-Wheat were calibrated for all five wheat cultivars using genetic coefficients estimated based upon measured data during 2008–09 cropping year and validated with independent data sets (experimental data of 2009–10 and 2010–11 cropping seasons) which were not used for models calibration. Both models were able to accurately simulate anthesis and maturity days, maximum leaf area index, biomass and grain yield, with normalized root mean square error (RMSE) less than 10%, D-index greater than 0.80 and model efficiency above 80% in most cases. The temporal changes in maximum LAI accumulation for all cultivars indicate that both measured and simulated values match each other. Evaluation with the measured data showed that performance of both models was realistic as indicated by the accurate simulation of crop phenology, LAI, biomass and grain yield against measured data. Climate variability results depicted that an increase in temperature from 0 °C to 5 °C resulted in a 60% average decline in the yield of wheat cultivars while increased CO 2 increased yield similar to the combined effect of increased temperature and CO 2 . We concluded that to bring accuracy in the simulation outcomes of models, new cultivars should be calibrated to minimize uncertainty to allow judicious recommendations in response to climate variability. [ABSTRACT FROM AUTHOR]

Highlights: [•] Wheat stem water soluble carbohydrates (WSC) contribute to yield under drought. [•] Developed calibration for WSC concentration and amount in wheat stems was done based on canopy proximal hyperspectral data, r 2 >0.88. [•] Reflectance bands in the model ranged from 350 to 1290nm; data spanned two seasons using lines contrasting in WSC, also including variation in crop developmental stages and management. [•] WSC can be modelled in parallel to Water Index or Enhanced Vegetation Index. [Copyright &y& Elsevier]

Using computer simulation model as a management tool requires model calibration and validation against field data. A three-year (2008–2009 to 2010–2011) field study was conducted at the Postgraduate Agricultural Research Station of the University of Agriculture, Faisalabad, Pakistan, to simulate wheat grain yield production as a function of urea fertilizer applications using Crop Environment REsource Synthesis (CERES)-Wheat model. The model was calibrated using yield data for treatment of urea fertilizer application at the rate of 247 kg-urea ha−1 during growing season 2009–2010 and was validated against independent data sets of yield of two years (2008–2009 and 2010–2011) for a wide variety of treatments ranging from no urea application to 247 kg-urea ha−1 application. The model simulations were found to be acceptable for calibration as well as validation period, as the model evaluation indicators showed a mean difference of 8.9%, ranging from 0.05 to 15.38%, root mean square error of 356 having its range from 242 to 471 kg ha−1, against all observed grain yield data. The scenario simulations showed maximum grain yield of 4100 kg ha−1 for 350 kg-urea ha−1 in 2008–2009; 4600 kg ha−1 for 300 kg-urea ha−1 in 2009–2010 and 5200 kg ha−1 for 340 kg-urea ha−1 in 2010–2011. Any further increase in urea application resulted in decline of grain yield function. These results show that model has the ability to simulate effects of urea fertilizer applications on wheat yield; however, the simulated maximum grain yield data need field-based verification. [ABSTRACT FROM AUTHOR]

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Abstract: Rheological properties of dough are important for wheat quality characterisation. This research sought to obtain prediction models for rheological characteristics and to characterise the breadmaking quality of whole wheat using NIRS (Near Infrared Reflectance Spectroscopy) technology, in order to offer a rapid tool to the farmers to know the quality of their product at the harvest moment. Tenacity (P), extensibility (L), deformation energy (W) and ratio P/L of dough were measured using traditional methods. NIR spectra were acquired from these samples. Models to predict the values from these parameters were developed. The SEC achieved for the extensibility, deformation energy and tenacity of dough and the ratio between the two latter parameters were 5.27mm, 9.97×10−4 J, 3.98mm and 0.025, respectively. The four models were validated by cross-validation, and by independent validation. The precision obtained in these models was enough for being applied in harvesters or at delivering moment. [Copyright &y& Elsevier]

Abstract: With increasing interest in retaining crop residues on the soil surface, there is a need to evaluate their short- and long-term effects on crop yield and water and fertilizer requirements. Therefore, research on the interactions between residue and irrigation management on wheat crop performance and water use was initiated, using the dual approach of field experiments and crop modelling. This paper presents the results of a comprehensive evaluation of the APSIM model for its ability to simulate the effects of mulch and water management, and their interactions, for wheat in Punjab, India. The model was evaluated for its ability to predict crop development, grain yield, biomass production over time, soil water dynamics, daily soil evaporation (Es), total evapotranspiration (ET) and water productivity (WPET kgha−1 mm−1), using two years of data from field experiments at Ludhiana, Punjab. The model predicted grain yield adequately, with coefficients of determination (r 2) of 0.91 and 0.81 with and without mulch, respectively, and prediction of total biomass was even better, with r 2 of 0.99 and 0.92. The corresponding absolute RMSE values were 433 and 550kgha−1 for grain yield (means 4100 and 3800kg ha−1) and 300 and 800kgha−1 for total biomass (means 10,200 and 9300kgha−1). However, grain yield was underpredicted (by 600–1000kgha−1) in treatments where the crop was subjected to water deficit stress, even though simulation of soil water dynamics, and the effect mulch on soil water content, was generally very good. The model accurately predicted total crop seasonal evaporation and the effect of mulch; however, daily Es was poorly simulated. APSIM does not attempt to capture the soil temperature driven effect of mulch on crop phenology. The evaluation shows that APSIM is suitable to use for wheat under the conditions of north-west India. However, additional model processes that capture the effects of mulch on crop development and growth, as driven by soil temperature, are needed to help design intensive cropping systems to optimise land and water productivity. The ability to better simulate crop performance under conditions of water deficit is also needed to help determine irrigation management strategies that minimise irrigation input while maintaining yield. [Copyright &y& Elsevier]

Abstract: The wavelet transform has been shown to be a useful tool for multivariate calibration. However, the choice of wavelet transform settings (wavelet family, length and number of decomposition levels) for a given application is still an open problem. The present paper proposes an alternative approach, which consists of generating an ensemble model by combining individual models obtained with different wavelet transform settings. The advantages of the proposed method are demonstrated in two analytical problems, namely the determination of moisture and protein in wheat by near infrared spectroscopy and the determination of specific mass and three distillation temperatures (T10, T50, T90) in gasoline by middle infrared spectroscopy. In these problems, the results varied considerably among individual models, which underlines the risk associated to an inadequate choice of wavelet transform settings. In contrast, the ensemble model always provided adequate results in terms of prediction error and noise sensitivity. The proposed method can be seen as an advantageous alternative for multivariate calibration in the wavelet domain, as it frees the analyst from the need to choose a particular configuration for the wavelet transform. [Copyright &y& Elsevier]

Abstract: The history of anti-obesity drug development is far from glorious, with transient magic bullets and only a handful of agents currently licensed for clinical use. In view of recent progress in our understanding of the multiplicity of signalling pathways involved in appetite regulation, and the resultant deluge of reports on the anorectic efficacy of novel therapies, it seems timely to stress the need to differentiate treatments that suppress intake by primary means from those that only indirectly achieve this endpoint. The current article reviews the conceptual history of the behavioural satiety sequence (BSS), also known as the behavioural sequence of satiety, post-ingestive satiety, and the postprandial satiety sequence. Early research confirmed that natural satiation, produced by a caloric load on the gut, is associated with a predictable transition from feeding through grooming to resting. Although many less naturalistic manipulations are also capable of reducing food intake, very few do so without disrupting the normal structure of this feeding cycle. Thus, while CCK and d-fenfluramine reduce intake by accelerating but otherwise maintaining the integrity of the BSS, other anorectic interventions disrupt the BSS through response competition (e.g. d-amphetamine), nausea/discomfort (e.g. lithium chloride) and/or interference with taste-mediated positive feedback (e.g. quinine adulteration of the diet). A substantial literature now strongly supports the specific involvement of serotonin 5-HT1B and 5-HT2C receptor subtypes in satiety and in the anorectic effect of agents such as fenfluramine and fluoxetine. Recent BSS analyses have also identified rather selective anorectic profiles for the dual noradrenaline and 5-HT reuptake inhibitor sibutramine, the orexin-1 receptor antagonist SB-334867, and the broad spectrum opioid receptor antagonist naloxone. However, similar analyses have offered little/no support for the anorectic potential of the gut peptide PYY3–36 while the acute anorectic efficacy of cannabinoid CB1 receptor antagonist/inverse agonists appears largely to be secondary to response competition. In contrast, studies with low-dose combinations of naloxone and CB1 receptor antagonist/inverse agonists have very recently confirmed the potential of drug polytherapies not only in appetite suppression but also in attenuating/eliminating unwanted side-effects. In sum, as BSS analysis offers a reliable means of differentiating the wheat (primary anorectics) from the chaff (secondary anorectics), it should form an integral part of early phase testing in any anti-obesity drug screening programme. [Copyright &y& Elsevier]

Abstract: A supramolecular solvent made up of reverse micelles of decanoic acid, dispersed in a continuous phase of THF:water, was proposed for the simple, fast and efficient microextraction of OTA in wheat prior to liquid chromatography-fluorescence determination. The method involved the stirring of 300 mg-wheat subsamples (particle size 50μm) and 350μL of supramolecular solvent for 15min, subsequent centrifugation for 15min and the direct quantitation of OTA in the extract, previous 5.7-fold dilution with ethanol/water/acetic acid (49.5/49.5/1), against solvent-based calibration curves. No clean-up of the extracts or solvent evaporation was needed. Interactions between the supramolecular solvent and major matrix components in the wheat (i.e. carbohydrates, lipids and proteins) were investigated. The reverse micelles in the extractant induced gluten flocculation but only in the coacervation region of lower analytical interest (i.e. at percentages of THF above 11%). The quantitation of OTA was interference-free. Representativity of the 300 mg-wheat subsamples was proved by analysing a reference material. OTA recoveries in wheat ranged between 84% and 95% and the precision of the method, expressed as relative standard deviation, was 2%. The quantitation limit of the method was 1.5μgkg−1 and was below the threshold limit established for OTA in raw cereals by EU directives (5.0μgkg−1). The method developed was validated by using a certified reference material and it was successfully applied to the determination of OTA in different wheat varieties from crops harvested in the South of Spain. OTA was not detected in any of the analysed samples. This method allows quick and simple microextraction of OTA with minimal solvent consumption, while delivering accurate and precise data. [Copyright &y& Elsevier]

Fourier transform near-infrared spectroscopy (FT-NIR) was used for rapid and non-invasive analysis of deoxynivalenol (DON) in durum and common wheat. The relevance of using ground wheat samples with a homogeneous particle size distribution to minimize measurement variations and avoid DON segregation among particles of different sizes was established. Calibration models for durum wheat, common wheat and durum + common wheat samples, with particle size <500 µm, were obtained by using partial least squares (PLS) regression with an external validation technique. Values of root mean square error of prediction (RMSEP, 306-379 µg kg-1) were comparable and not too far from values of root mean square error of cross-validation (RMSECV, 470-555 µg kg-1). Coefficients of determination (r2) indicated an “approximate to good” level of prediction of the DON content by FT-NIR spectroscopy in the PLS calibration models (r2 = 0.71-0.83), and a “good” discrimination between low and high DON contents in the PLS validation models (r2 = 0.58-0.63). A “limited to good” practical utility of the models was ascertained by range error ratio (RER) values higher than 6. A qualitative model, based on 197 calibration samples, was developed to discriminate between blank and naturally contaminated wheat samples by setting a cut-off at 300 µg kg-1 DON to separate the two classes. The model correctly classified 69% of the 65 validation samples with most misclassified samples (16 of 20) showing DON contamination levels quite close to the cut-off level. These findings suggest that FT-NIR analysis is suitable for the determination of DON in unprocessed wheat at levels far below the maximum permitted limits set by the European Commission. [ABSTRACT FROM AUTHOR]

In the first part of this work, the shoot growth module and grain yield of the STICS crop model were calibrated and validated by using field data which was collected from irrigated winter wheat fields in the Haouz plain near Marrakech. The calibration was performed on the thermal units between the four phenological stages that control the dynamics of leaf area index and the thermal unit between emergence and the beginning of grain filling. The plant phenology was calibrated for three fields monitored during the 2002/03 season. Evaluation of the grain yields and the temporal evolution of leaf area index were done for six validation fields during 2003/04. The results showed the significant accuracy of the model in simulating these variables, and also indicated that the plants mainly suffered from lack of nitrogen. The results in the second part show the potential of crop modeling to schedule irrigation water, on the assumption that the plants were growing under optimal conditions of fertilization. In this case, the model was used to manage the time of irrigation according to a threshold for water deficit. Various simulations displayed logical trends in the relationship between the grain yield and both the amount and timing of irrigation water. These results were finally compared with those obtained from real irrigation practices. For the particular climate of 2003/04, the comparison showed that 70 mm and 40 mm of water could be saved in case of early and late sowing, respectively. [ABSTRACT FROM AUTHOR]

Abstract: A spatially referenced biophysical model, the water and nitrogen management model (WNMM), was developed and shown to simulate dynamic soil water movement and soil–crop carbon (C) and nitrogen (N) cycling under a given agricultural management, for the purpose of identifying optimal strategies for managing water and fertiliser N under intensive cropping systems (mainly wheat–maize) in the North China Plain and other regions in the world. A uniform data structure, ARC GRID ASCII format, was used both in GIS and WNMM for achieving a close Model-GIS coupling. A significant part of WNMM adopts and modifies concepts and components from widely used models, with a focus on soil N transformations. WNMM simulates the key processes of water dynamics in the surface and subsurface of soils: including evapotranspiration, canopy interception, water movement and groundwater fluctuations; heat transfer and solute transport; crop growth; C and N cycling in the soil–crop system; and agricultural management practices (crop rotation, irrigation, fertiliser application, harvest and tillage). The model runs on a daily time step at any desired scale and is driven by lumped variables (meteorological and crop biological data) in text data format, and spatial variables (soil and agricultural management) in ARC GRID ASCII format. In particular, WNMM simulates all key N transformations in agricultural fields, including mineralisation of fresh crop residue N and soil organic N, formation of soil organic N, immobilisation in biomass, nitrification, ammonia (NH3) volatilisation, denitrification and nitrous oxide (N2O) emissions. WNMM has been successfully applied in Fengqiu County, Henan Province and Luancheng County, Hebei Province, China at site and regional scales. The key WNMM components were intensively calibrated and verified against comprehensive field measurements of soil water content, evapotranspiration, crop leaf area index and yield, NH3 volatilisation, denitrification and N2O emissions as well as nitrate (NO3 −)–N concentrations in the soil solution. A sensitivity analysis showed that WNMM was sensitive to changes in meteorological variables, soil hydraulic properties, land use and agricultural management. At the site scale, WNMM simulated well soil water content, crop growth and yield, NH3 volatilisation and soil NO3 −–N concentration. There was uncertainty in simulating soil denitrification and N2O emissions, when the predicted peaks of denitrification and N2O emissions at very wet conditions could not be confirmed because of limitations in the acetylene-inhibition method for measuring denitrification. At county scale, WNMM simulation of crop yield in Fengqiu County explained 22% of the variation in observed crop yields, and 31% of crop yield variation in Luancheng County where soil variation was less. These results are considered acceptable because factors such as soil salinity and other nutrient deficiencies, which have not been considered in this version of WNMM, may play a role. The source code for the WNMM is available on request. [Copyright &y& Elsevier]

Determination of wheat flour quality parameters, such as protein, moisture, dry mass by wet chemistry analyses takes long time. Near infrared spectroscopy (NIR) coupled with multivariate calibration offers a fast and nondestructive alternative to obtain reliable results. However, due to the complexity of the spectra obtained from NIR, some wavelength selection is generally required to improve the predictive ability of multivariate calibration methods. In this study, two different wheat data sets are investigated with the aim of establishing successful calibration models using NIR spectra of wheat samples. The first data set (material 1) was obtained from the ftp address ( ) and contained 100 NIR spectra of wheat of which wet chemical analysis of protein and moisture content were done with reference methods. The second data set (material 2) contained 176 spectra and was downloaded from . This wheat data set was given with the quality parameters, such as protein content, moisture content, other residues, dry mass, protein content in dry mass and hardness that were determined previously. Multivariate calibration models generated with genetic inverse least squares method demonstrated very good prediction results for the parameter mentioned here. Overall, the average per cent recoveries (APR) ranged between 99.23% and 100.34% with a standard deviation (SD) ranging from 0.34 to 3.15 for all the parameters investigated, except hardness. The APR value of hardness was 103.32 with the SD of 14.97. [ABSTRACT FROM AUTHOR]

Flour quality control needs quick analytical tools for predicting rheological and chemical properties. In routine flour quality, wet chemistry analyses take more time. NIR technology allows us to obtain results in a few seconds. In this study commercial wheat flour samples were characterized in terms of protein, moisture, dry gluten, wet gluten, starch damage and ash contents. In addition, wheat flour dough rheological tests were assessed by farinograph and alveograph. Modified partial least squares analyses on NIR transmittance spectroscopy were developed for each constituent or property. Some NIR models obtained were accurate enough for screening end-use flour quality proposes purposes. [Copyright &y& Elsevier]