Recycling of sugar crop disposal to boost the adaptation of canola (Brassica napus L.) to abiotic stress through different climate zones

Canola Seed Protein: Pretreatment, Extraction, Structure, Physicochemical and Functional Characteristics

The rapid growth of the global population has led to an unprecedented demand for dietary protein. Canola seeds, being a widely utilized oil resource, generate substantial meal by-products following oil extraction. Fortunately, canola meals are rich in protein. In this present review, foremost attention is directed towards summarizing the characteristics of canola seed and canola seed protein. Afterwards, points of discussion related to pretreatment include an introduction to pulsed electric field treatment (PEF), microwave treatment (MC), and ultrasound treatment (UL). Then, the extraction method is illustrated, including alkaline extraction, isoelectric precipitation, acid precipitation, micellization (salt extraction), and dry fractionation and tribo-electrostatic separation. Finally, the structural complexity, physicochemical properties, and functional capabilities of rapeseed seeds, as well as the profound impact of various applications of rapeseed proteins, are elaborated. Through a narrative review of recent research findings, this paper aims to enhance a comprehensive understanding of the potential of canola seed protein as a valuable nutritional supplement, highlighting the pivotal role played by various extraction methods. Additionally, it sheds light on the broad spectrum of applications where canola protein demonstrates its versatility and indispensability as a resource.

Optimizing sowing window, cultivar choice, and plant density to boost maize yield under RCP8.5 climate scenario of CMIP5

The impacts of climate change and possible adaptations to food security are a global concern and need greater focus in arid and semi-arid regions. It includes scenario of Coupled Model Intercomparison Phase 5 (CMIP-RCP8.5). For this purpose, two DSSAT maize models (CSM-CERES and CSM-IXIM) were calibrated and tested with two different maize cultivars namely Single Cross 10 (SC10) and Three Way Cross 324 (TW24) using a dataset of three growing seasons in Nile Delta. SC10 is a long-growing cultivar that is resistant to abiotic stresses, whereas TW24 is short and sensitive to such harsh conditions. The calibrated models were then employed to predict maize yield in baseline (1981-2010) and under future time slices (2030s, 2050s, and 2080s) using three Global Climate Models (GCMs) under CMIP5-RCP8.5 scenario. In addition, the use of various adaptation options as shifting planting date, increasing sowing density, and genotypes was included in crop models. Simulation analysis showed that, averaged over three GCMs and two crop models, the yield of late maturity cultivar (SC10) decreased by 4.1, 17.2, and 55.9% for the three time slices of 2030s, 2050s, and 2080s, respectively, compared to baseline yield (1981-2010). Such reduction increased with early maturity cultivar (TW24), recording 12.4, 40.6, and 71.3% for near (2030s), mid (2050s), and late century (2080s) respectively relative to baseline yield. The most suitable adaptation options included choosing a stress-resistant genotype, changing the planting date to plus or minus 30 days from baseline planting date, and raising the sowing density to 9 m^-2 plants. These insights could minimize the potential reduction of climate change-induced yields by 39% by late century.

Identification of drought tolerant Chickpea genotypes through multi trait stability index

Drought is a major and constantly increasing abiotic stress factor, thus limiting chickpea production. Like other crops, Kabuli Chickpea genotypes are screened for drought stress through Multi-environment trials (METs). Although, METs analysis is generally executed taking into account only one trait, which provides less significant reliability for the recommendation of genotypes as compared to multi trait-based analysis. Multi trait-based analysis could be used to recommend genotypes across diverse environments. Hence, current research was conducted for selection of superior genotypes through multi-trait stability index (MTSI) by using mixed and fixed effect models under six diverse environments. The genotypic stability was computed for all traits individually using the weighted average of absolute scores from the singular value decomposition of the matrix of best linear unbiased predictions for the genotype vs environment interaction (GEI) effects produced by a linear mixed-effect model index. A superiority index, WAASBY was measured to reflect the MPS (Mean performance and stability). The selection differential for the WAASBY index was 11.2%, 18.49% and 23.30% for grain yield (GY), primary branches per plant (PBP) and Stomatal Conductance (STOMA) respectively. Positive selection differential (0.80% ≤ selection differential ≤ 13.00%) were examined for traits averaged desired to be increased and negative (-0.57% ≤ selection differential ≤ -0.23%) for those traits desired to be reduced. The MTSI may be valuable to the plant breeders for the selection of genotypes based on many characters as being strong and simple selection process. Analysis of MTSI for multiple environments revealed that, the genotypes G20, G86, G31, G28, G116, G12, G105, G45, G50, G10, G30, G117, G81, G48, G85, G17, G32, G4, and G37 were the most stable and high yielding out of 120 chickpea genotypes, probably due to high MPS of selected traits under various environments. It is concluded that identified traits can be utilized as genitors in hybridization programs for the development of drought tolerant Kabuli Chickpea breeding material.

Sensitivity analysis of the CROPGRO-Canola model in China: A case study for rapeseed

Increasing domestic rapeseed production is an important national goal in China. Researchers often use tools such as crop models to determine optimum management practices for new varieties to increased production. The CROPGRO-Canola model has not been used to simulate rapeseed in China. The overall goal of this work was to identify key inputs to the CROPGRO-Canola model for calibration with limited datasets in the Yangtze River basin. First, we conducted a global sensitivity analysis to identify key genetic and soil inputs that have a large effect on simulated days to flowering, days to maturity, yield, above-ground biomass, and maximum leaf area index. The extended Fourier amplitude test method (EFAST) sensitivity analysis was performed for a single year at 8 locations in the Yangtze River basin (spatial analysis) and for seven years at a location in Wuhan, China (temporal analysis). The EFAST software was run for 4520 combinations of input parameters for each site and year, resulting in a sensitivity index for each input parameter. Parameters were ranked using the top-down concordance method to determine relative sensitivity. Results indicated that the model outputs of days to flowering, days to maturity, yield, above-ground biomass, and maximum leaf area index were most sensitive to parameters that affect the duration of critical growth periods, such as emergence to flowering, and temperature response to these stages, as well as parameters that affect total biomass at harvest. The five model outputs were also sensitive to several soil parameters, including drained upper and lower limit (SDUL and SLLL) and drainage rate (SLDR). The sensitivity of parameters was generally spatially and temporally stable. The results of the sensitivity analysis were used to calibrate and evaluate the model for a single rapeseed experiment in Wuhan, China. The model was calibrated using two seasons and evaluated using three seasons of data. Excellent nRMSE values were obtained for days to flowering (≤1.71%), days to maturity (≤ 1.48%), yield (≤ 9.96%), and above-ground biomass (≤ 9.63%). The results of this work can be used to guide researchers on model calibration and evaluation across the Yangtze River basin in China.

Developing new lines of Japonica rice for higher quality and yield under arid conditions

Rice is the world's largest food crop, and its production needs to be doubled by 2050 to cope with population growth and associated demand. In addition to the value of improving yields, quality is also important for breeders and consumers, but it pays less attention in arid regions. During two successive summer growing seasons, the experimental material focused on 34 genotypes developed from different crosses on Fn generation after fixation as well as six of the most recent commercial cultivars used for comparisons. The results showed that a high yield of grain followed by high milling and grain quality characteristics were observed among the 34 genotypes used in this analysis. Highly important and positive correlations between the percentage of hulling and the percentage of milling (0.424) and the yield ability could be accomplished by choosing the number of panicles per plant and the weight of the panicles. Selection criteria for good quality should be met by the percentage of head rice and many mineral elements, particularly zinc and iron. As a consequence, the genotypes M.J 5460S/SK105-1, M.J 5460S/GZ7768-1, M.J 5460S/G177-1, M.J 5460S/SK105-3 and M.J 5460S/SK106-4 had desirable high yield and quality characteristics and could be used as promising accessions to the rice breeding program in arid regions. In addition to commercial genotypes, improved Japonica rice genotypes could be produced in arid conditions for higher yield and quality, leading to an increase in total production, supporting food security and nutrition.