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Climate change poses emerging threats to wheat growth in coming future. These threats need to be explored to ensure sustainable wheat production. To do this, the SALTMED model was calibrated using data from experiments conducted on different levels of irrigation and nitrogen doses. The performance of the SALTMED model was assessed based on values of the root mean square error (RMSE), normalized root mean square error (NRMSE), coefficient of determination (R2) and coefficient of residual mass (CRM) that ranged from 0.23–1.82, 0.09–0.17, 0.91–0.93 and −0.01–0.02, respectively for calibration and 0.31–1.89, 0.11–0.31, 0.87–0.90 and −0.02–0.01, respectively for validation. Projections for future climate scenarios for wheat growth indicated that by the end of the century, sowing dates advanced by nine days under the RCP4.5 scenario and eleven days under the RCP8.5 scenario, while harvesting dates shifted earlier by twenty-four days under RCP4.5 and twenty-eight days under RCP8.5. Consequently, the overall crop duration was shortened by fifteen days under RCP4.5 and eighteen days under RCP8.5. Further simulations revealed that the wheat yield was reduced by 14.2% under RCP4.5 and 21.0% under RCP8.5; the dry matter was reduced by 14.9% under RCP4.5 and 23.3% under RCP8.5; the irrigation amount was expected to increase by 14.9% under RCP4.5 and 18.0% under RCP8.5; and water productivity was expected to be reduced by 25.3% under RCP4.5 and 33.0% under RCP8.5 until the end of century. The hypothetical scenarios showed that adding an extra 20–40% more nitrogen can enhance wheat yield and dry matter by 10.2–23.0% and 11.5–24.6%, respectively, under RCP4.5, and by 12.0–23.4% and 12.9–29.6%, respectively, under RCP8.5. This study offers valuable insights into the effects of climate change on future wheat production so that effective contingency plans could be made by policymakers and adopted by stakeholders for higher wheat productivity. [ABSTRACT FROM AUTHOR]

Climate change poses emerging threats to wheat growth in coming future. These threats need to be explored to ensure sustainable wheat production. To do this, the SALTMED model was calibrated using data from experiments conducted on different levels of irrigation and nitrogen doses. The performance of the SALTMED model was assessed based on values of the root mean square error (RMSE), normalized root mean square error (NRMSE), coefficient of determination (R2) and coefficient of residual mass (CRM) that ranged from 0.23–1.82, 0.09–0.17, 0.91–0.93 and −0.01–0.02, respectively for calibration and 0.31–1.89, 0.11–0.31, 0.87–0.90 and −0.02–0.01, respectively for validation. Projections for future climate scenarios for wheat growth indicated that by the end of the century, sowing dates advanced by nine days under the RCP4.5 scenario and eleven days under the RCP8.5 scenario, while harvesting dates shifted earlier by twenty-four days under RCP4.5 and twenty-eight days under RCP8.5. Consequently, the overall crop duration was shortened by fifteen days under RCP4.5 and eighteen days under RCP8.5. Further simulations revealed that the wheat yield was reduced by 14.2% under RCP4.5 and 21.0% under RCP8.5; the dry matter was reduced by 14.9% under RCP4.5 and 23.3% under RCP8.5; the irrigation amount was expected to increase by 14.9% under RCP4.5 and 18.0% under RCP8.5; and water productivity was expected to be reduced by 25.3% under RCP4.5 and 33.0% under RCP8.5 until the end of century. The hypothetical scenarios showed that adding an extra 20–40% more nitrogen can enhance wheat yield and dry matter by 10.2–23.0% and 11.5–24.6%, respectively, under RCP4.5, and by 12.0–23.4% and 12.9–29.6%, respectively, under RCP8.5. This study offers valuable insights into the effects of climate change on future wheat production so that effective contingency plans could be made by policymakers and adopted by stakeholders for higher wheat productivity.

Prospect of grain weight improvement in bread wheat has been reviewed by analysing 30-year data of the released and pre-released varieties evaluated in India during 1992–2021 under 10 diverse environments in five zones under timely and late sown conditions. The study reveals that enlargement of the reproductive phase by early heading and late maturity is feasible only in the environments where vegetative phase is large. This strategy of grain weight improvement can be effective in the northern hills of India for both timely sown (second fortnight of October) and late sown (first fortnight of December) wheat and it causes no reduction in the yield. This approach is also applicable in the agro-ecologies highly congenial for wheat growth as noticed in timely sown wheat varieties of western Indo-Gangetic plains. In areas where crop duration is short due to warmer climatic conditions like central and peninsular India, early heading can be effective for increasing grain weight, but it affects yield. Besides phenology, height can also be crucial in certain environments as it is closely related with grain weight in timely sown wheat of eastern as well as western Indo-Gangetic plains and late sown wheat of the northern hills. Height can also enhance the effect of phenological attributes in some environments. Under late heat-stressed environments, none of these parameters can be helpful in grain weight improvement. The study suggests that simultaneous improvement in grain weight and grain yield of wheat by the adoption of these predictors individually or in combination is possible in certain agro-ecologies of India. [ABSTRACT FROM AUTHOR]

Field-scale prediction methods that use remote sensing are significant in many global projects; however, the existing methods have several limitations. In particular, the characteristics of smallholder systems pose a unique challenge in the development of reliable prediction methods. Therefore, in this study, a fast and reproducible new approach to wheat prediction is developed by combining predictors derived from optical (Sentinel-2) and radar (Sentinel-1) sensors using a diverse set of machine learning and deep learning methods under a small dataset domain. This study takes place in the wheat belt region of Ethiopia and evaluates forty-two predictors that represent the major vegetation index categories of green, water, chlorophyll, dry biomass, and VH polarization SAR indices. The study also applies field-collected agronomic data from 165 farm fields for training and validation. According to results, compared to other methods, a combined automated machine learning (AutoML) approach with a generalized linear model (GLM) showed higher performance. AutoML, which reduces training time, delivered ten influential parameters. For the combined approach, the mean RMSE of wheat yield was from 0.84 to 0.98 ton/ha using ten predictors from the test dataset, achieving a 99% confidence interval. It also showed a correlation coefficient as high as 0.69 between the estimated yield and measured yield, and it was less sensitive to the small datasets used for model training and validation. A deep neural network with three hidden layers using the ten influential parameters was the second model. For this model, the mean RMSE of wheat yield was between 1.31 and 1.36 ton/ha on the test dataset, achieving a 99% confidence interval. This model used 55 neurons with respective values of 0.1, 0.5, and 1 × 10−4 for the hidden dropout ratio, input dropout ratio, and l2 regularization. The approaches implemented in this study are fast and reproducible and beneficial to predict yield at scale. These approaches could be adapted to predict grain yields of other cereal crops grown under smallholder systems in similar global production systems. [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]

In the global context of population growth and climate change, monitoring crops is necessary to sustain agriculture and conserve natural resources. While many studies have demonstrated the ability of optical and SAR remotely sensed data to estimate crop parameters, these data have not been compared or combined to predict crop phenological stages. Despite the high sensitivity of SAR polarimetric data to crop phenological stages, no study has used high temporal resolution data. The freely available SAR Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a unique opportunity to monitor crop phenology at a high spatial resolution on a weekly basis. The objective of this study was to evaluate the potential of S-1 data alone, S-2 data alone, and their combined use to predict wheat and rapeseed phenological stages. We first analyzed temporal profiles of spectral bands, vegetation indices and leaf area index (LAI) derived from S-2 data, and backscattering coefficients and polarimetric indicators derived from S-1 data. Then, an incremental procedure was used to estimate the contribution of S-1 and S-2 features to the classification of principal and secondary phenological stages of wheat and rapeseed. Results for both crops showed that the classification obtained with combined S-1 & 2 data (mean kappa = 0.53–0.82 and 0.74–0.92 for wheat and rapeseed, respectively) was more accurate than those obtained with S-2 data alone (mean kappa = 0.54–0.75 and 0.67–0.86 for wheat and rapeseed, respectively) or S-1 data alone (mean kappa = 0.48–0.61 and 0.61–0.64 for wheat and rapeseed, respectively). Combining S-1 & 2 data allowed better identification of the beginning and end of tillering for wheat and the beginning and end of ripening for rapeseed. Among S-2 features, the most important were LAI for wheat and the NDVI for rapeseed. For both crops, the S2REP index was one of the most important vegetation indices, while MCARI was less important. For S-1 features, results highlighted the large contribution of the backscatter ratio (σ◦VH:σ◦VV) and the value of using polarimetric indicators (Shannon entropy and span) to monitor rapeseed and wheat phenology. The main novelties of this work are the use of S-1 polarimetric indicators to identify phenological stages of wheat and rapeseed and the mapping of wheat and rapeseed secondary phenological stages using remotely sensed data. [ABSTRACT FROM AUTHOR]

Information on crop phenological development stages such as emergence, flowering, fruiting, maturing and senescence is essential for crop production surveillance and yield prediction. It has long been related to optical spectral signatures such as the Normalized Difference Vegetation Index (NDVI) or spectral shifts in the red-edge range. In recent years, more efforts have been made to explore the sensitivity of Synthetic Aperture Radar (SAR), particularly polarimetric SAR signatures, to crop biophysical parameters or phenological stages. In this study, phenological metrics of canola ( Brassica napus ) and spring wheat ( Triticum spp.) are related with temporal evolution of polarimetric SAR parameters derived from the C-band RADARSAT-2 full polarimetric SAR data. Both crops are very common in north eastern Ontario, Canada, but have very anatomically different development processes. From multi-temporal RADARSAT-2 data acquired in three consecutive years (2012–2014), significant correlations were observed between a number of SAR polarimetric parameters and the growth parameters of both crops. Strong correlation was observed between plant height and the Alpha angle of the Cloude-Pottier decomposition, with the R 2 of 0.91 and 0.66 for canola and wheat, respectively. The R 2 increased when the polarimetric parameters were smoothed in the time domain (R 2 of 0.98 for canola and 0.88 for wheat). Strong correlation was also observed for the two crops between the effective leaf area index (LAIe) and the Beta angle, and between days-after-seeding (DAS) and a combination of the Alpha and the Beta angles. These findings show that multi-temporal C-band polarimetric SAR parameters could be used for tracking crop phenological development stages. [ABSTRACT FROM AUTHOR]

High temperature is a major determinant of wheat development and growth and causes yield loss in many regions of the world. The present study was aimed at assessing the effects of heat stress on phenologic, floral, yield and seed quality attributes of fourteen wheat genotypes under four environmental conditions; raised at two dates: November (favourable) and January (heat stress) during rabi seasons of 2012-13 and 2013-14. Under heat stress condition, 5-12°C higher temperatures were recorded at anthesis and post-anthesis stage than the normal sown. The study revealed significant differences among dates of sowing, thereby indicating sensitivity to heat stress. Among the plant morphological traits, there was a maximum reduction in plant height under heat stress condition (January sown) as compared to normal sown condition.Phenology of wheat crop was also significantly affected by higher temperature. Wheat varieties sown during the month of November took more time to reach maturity while late sown material recorded lesser crop growth duration. Among the seed yield and quality parameters, high temperature caused maximum reduction in tiller number (70%) followed by grain yield (67.3% reduction), 1000-grain weight (47% reduction) and vigour Index II (52.7% reduction). Hence, these traits were identified as the most susceptible traits to heat stress. Heat stress also caused a reduction of 39.7 per cent for canopy temperature depression at grain filling stage. Flag leaf area, spikelet fertility and pollen fertility reduced by 33.1%, 22.7% and 7.7%, respectively, thereby indicating that these traits were less affected due to higher temperature.Genotypes GW 366 and HD 2888 showed least heat susceptibility index (HSI)for grain yield whereas for seed quality, HD 3093 showed better tolerance compared to others.

High temperature is a major determinant of wheat development and growth and causes yield loss in many regions of the world. The present study was aimed at assessing the effects of heat stress on phenologic, floral, yield and seed quality attributes of fourteen wheat genotypes under four environmental conditions; raised at two dates: November (favourable) and January (heat stress) during rabi seasons of 2012-13 and 2013-14. Under heat stress condition, 5-12°C higher temperatures were recorded at anthesis and post-anthesis stage than the normal sown. The study revealed significant differences among dates of sowing, thereby indicating sensitivity to heat stress. Among the plant morphological traits, there was a maximum reduction in plant height under heat stress condition (January sown) as compared to normal sown condition.Phenology of wheat crop was also significantly affected by higher temperature. Wheat varieties sown during the month of November took more time to reach maturity while late sown material recorded lesser crop growth duration. Among the seed yield and quality parameters, high temperature caused maximum reduction in tiller number (70%) followed by grain yield (67.3% reduction), 1000-grain weight (47% reduction) and vigour Index II (52.7% reduction). Hence, these traits were identified as the most susceptible traits to heat stress. Heat stress also caused a reduction of 39.7 per cent for canopy temperature depression at grain filling stage. Flag leaf area, spikelet fertility and pollen fertility reduced by 33.1%, 22.7% and 7.7%, respectively, thereby indicating that these traits were less affected due to higher temperature.Genotypes GW 366 and HD 2888 showed least heat susceptibility index (HSI)for grain yield whereas for seed quality, HD 3093 showed better tolerance compared to others. [ABSTRACT FROM AUTHOR]

The observed historical changes in crop phenological events are not only as a sensitive indicator to temperature variations but also the consequence of agronomic management such as adjustment of sowing date, cultivar selection and innovative farming practices. Clarifying how these factors influence crop phenology is of paramount importance in understanding/implementing adaptation strategies to future climate change. Changes in phenophases of dryland spring-sown wheat and maize were investigated using the observed phenological data from 1992 to 2013 across the Loess Plateau. Using a series of phenological growth models, we illustrated that once the varietal effect was fixed, rising temperature alone produced a general advancement on dates of heading and maturity, leading to a significant shortening of the vegetative phase (5.2 days decade −1 in wheat and 1.1 days decade −1 in maize), a shortened duration of reproductive phase (0.9 and 2.0 days decade −1 respectively) and the length of whole growing season (6.1 days decade −1 in wheat and 3.0 days decade −1 in maize). In contrast, cultivar shifts prolonged the reproductive duration and the growing season length by 2.2 and 4.7 days decade −1 for spring wheat, and 3.3 and 1.5 days decade −1 for maize, respectively. The continuous update of cultivars during the past two decades has offset 63.5% of the shortening effect of growing season length caused by rising temperature for both crops. Our data indicate that adjusting sowing date and shifts to longer season cultivars are the critical adaptive strategies to cope with temperature increases in dryland spring-sown wheat and maize in the Loess Plateau or regions with similar environments. [ABSTRACT FROM AUTHOR]

An analysis of the sensitivity of synthetic aperture radar (SAR) backscatter (σo) to crop and soil conditions was conducted using 57 RADARSAT-2 C-band quad-polarized SAR images acquired from April to September 2009 for large fields of wheat, barley, oat, corn, onion, and alfalfa in Barrax, Spain. Preliminary results showed that the cross-polarized σHVo was particularly useful for monitoring both crop and soil conditions and was the least sensitive to differences in beam incidence angle. The greatest separability of barley, corn, and onion occurred in spring after the barley had been harvested or in the narrow time window associated with grain crop heading when corn and onion were still immature. The time series of σo offered reliable information about crop growth stage, such as jointing and heading in grain crops and leaf growth and reproduction in corn and onion. There was a positive correlation between σo and the Normalized Difference Vegetation Index for onion and corn but not for all crops, and the impact of view direction and incidence angle on the time series was minimal compared to the signal response to crop and soil conditions. Related to planning for future C-band SAR missions, we found that quad-polarization with image acquisition frequency from 3-6 days was best suited for distinguishing crop types and for monitoring crop phenology, single- or dual-polarization with an acquisition frequency of 3-6 days was sufficient for mapping crop green biomass, and single- or dual-polarization with daily image acquisition was necessary to capture rapid changes in soil moisture condition. [ABSTRACT FROM AUTHOR]

• Improved shape model is better in detecting growth stages when compared with previous methods. • ASD is better than UAV-DC and Greenseeker in detecting all growth stages except the tillering stage. • Simulated satellite index (EVI) from the MODIS has the best potential to detect all stages at large scales. • The accuracy of phenology detecting is highly related to the form and characteristics for the shape model. Accurate information about the growth period can guide us to fertilize, irrigate and harvest. Much progress has been achieved in detecting the phenology with the unique features of the time-series vegetation index (VI). However, these features only reflect information about specific stages (e.g., tillering, heading, and maturity stages), ignoring the information from other important stages (e.g., jointing, booting, and filling stages). In this study, a new approach for the phenology detection of winter wheat at the whole phenological stages is described, whereby the integrated accumulated growing degree days (AGDD) combined with the shape model (SM) method (SM-AGDD) is used to detect important phenology stages of winter wheat using five classic time-series VIs derived from three sensors at the field scale. Two proximal sensors (ASD FieldSpec Pro spectrometer and a Greenseeker RT 100) and a digital camera mounted on an unmanned aerial vehicle (UAV-DC) are used to acquire the above time-series VI. The results show that the newly developed SM-AGDD with the normalized difference vegetation index (NDVI) from ASD is the best predictor of crop phenology with an average RMSE ranging from 1.0 day at maturity to 10.3 days at tillering, followed by CI, EVI, and VARI, respectively. Among the three different spectral sensors, ASD has the best performance for detecting the whole targeted stages, while UAV-DC was the worst. In particular, the accuracy of EVI has the highest improvement on all growth stages. Compared with the previous SM constructed with the days after sowing (DAS) produced by Sakamoto et al. (2010), the newly developed SM-AGDD improves the accuracy of detecting the critical stages for winter wheat phenology for all VIs. We also find that SM-AGDD has a higher accuracy to the SM constructed with accumulated photothermal time (APTT) by Zeng et al. (2016). While, it also greatly simplified the calculation. This study shows that the accuracy of the shape model method is affected by the form and characteristics of the constructed shape, which could provide the theoretical basis for accurate detection of critical phenology dates for crops. [ABSTRACT FROM AUTHOR]

• Appraising cadmium mobility and transfer in soil-plant system is imperative to ensure food safety. • Plant phenology can serve as novel and alternative approach for grain cadmium prediction. • Cadmium mobility and transfer from soil-to-wheat grain is co-regulated by soil-plant-N management factors. Appraising cadmium (Cd) phytoavailability and transfer in soil-plant system is imperative and it requires timely and accurate monitoring of Cd to ensure food safety. However, ambiguities regarding the factors regulating Cd mobility and transfer in soil-plant system makes understanding of Cd accumulation mechanism in wheat grain challenging. In present study, we attempted to explore the interrelationship among soil-plant-N management factors governing Cd transfer from soil-to-wheat grain and to provide a novel and alternative approach for grain Cd prediction. For this purpose, we established the allometric relationships of wheat phenology (plant dry matter at different growth stages and grain yield) with grain Cd concentration and soil properties (pH, EC, Eh, and CEC) under varied N rates experiment and investigated the interactions among aforementioned factors. The newly established allometric relationships demonstrated that plant phenology and yield were positively correlated with grain Cd concentration (R2 = 0.86–0.95) and soil properties (R2 = 0.84–0.97). Robust interrelationship among soil-plant-N management factors indicated that Cd transfer from soil-to-wheat grain was potentially co-regulated by their interactive effect. Findings will assist to strategize crop productivity and soil sustainability without compromising food safety. Further studies are imperative to better understand the Cd uptake mechanism in different wheat cultivars and management practices. [ABSTRACT FROM AUTHOR]

• A plastic phenology model was developed for simulating crop reproductive phase. • The model assumes a dynamic response of development rate to temperature. • Wheat, rice, and maize phenology observations under varied conditions were used. • The model effectively reduced phenology simulation error in a simple and effective way. Understanding how crop development rate responds to the environment provides the basis for evaluating the impact of climate change on crop yield. In most crop simulation models, temperature response functions of development rate during the reproductive growth period (RGP) are assumed to only vary with temperature and not with other environmental factors. However, studies have indicated that the response functions may be plastic with other factors. Until now, little attention has been paid to this type of response. Here, using extensively collected field observations and data from intentionally designed interval planting experiments with winter wheat (Triticum aestivum L.), rice (Oryza sativa L.), and spring maize (Zea mays L.), we show that temperature response functions during RGP are plastic with day of year of flowering/heading (DOY R). Coefficients of determination between DOY R and development rate were significant for 69% sites. Partial correlation coefficients between development rate, temperature, and DOY R suggest that DOY R explains almost the same variability in maturity date as temperature. The plastic model was developed by coupling DOY R with a linear temperature response function. The model can improve the fitting efficiency by 112%, while dependency between DOY R and temperature explains less than 25% of this improvement. The average RMSEs of simulated maturity date estimated by the plastic model in the three crops were 2.1, 2.5, and 3.7 d, respectively, while the corresponding values given by widely applied traditional models were 3.1, 6.5, and 7.4 d, respectively. Therefore, the plastic model can reduce simulation error by half. Moreover, simulation errors resulting from the plastic model have less systematic bias than traditional models. The plastic model simply and effectively provides accurate estimates of crop maturity and reduces the system deviation of the estimates. Coupling the plastic model of crop development with crop simulation models will likely decrease uncertainties in simulated yield under warming conditions. Additionally, results of this study will encourage future studies of other phenotype plasticity considered in current crop simulation models. [ABSTRACT FROM AUTHOR]