Prediction Of Grain Protein Content Research Articles

Improving the Prediction of Grain Protein Content in Winter Wheat at the County Level with Multisource Data: A Case Study in Jiangsu Province of China

Wheat is an important food crop in China. The quality of wheat affects the development of the agricultural economy. However, the high-quality wheat produced in China cannot meet the demand, so it would be an important direction for research to develop high-quality wheat. Grain protein content (GPC) is an important criterion for the quality of winter wheat and its content directly affects the quality of wheat. Studying the spatial heterogeneity of wheat grain proteins is beneficial to the prediction of wheat quality, and it plays a guiding role in the identification, grading, and processing of wheat quality. Due to the complexity and variability of wheat quality, conventional evaluation methods have shortcomings such as low accuracy and poor applicability. To better predict the GPC, geographically weighted regression (GWR) models, multiple linear regression, random forest (RF), BP neural networks, support vector machine, and long-and-short-term memory algorithms were used to analyze the meteorological data and soil data of Jiangsu Province from March to May in 2019–2022. It was found that the winter wheat GPC rises by 0.17% with every 0.1° increase in north latitude at the county level in Jiangsu. Comparison of the prediction accuracy of the coefficient of determination, mean deviation error, root mean square error, and mean absolute error by analyzing multiple algorithms showed that the GWR model was the most accurate, followed by the RF model. The regression coefficient of precipitation in April showed the smallest range of variation among all factors, indicating that precipitation in April had a more stable effect on GPC in the study area than the other meteorological factors. Therefore, consideration of spatial information might be beneficial in predicting county-level winter wheat GPC. GWR models based on meteorological and soil factors enrich the studies regarding the prediction of wheat GPC based on environmental data. It might be applied to predict winter wheat GPC and improve wheat quality to better guide large-scale production and processing.

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals.

Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R2 ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade.

Genomic Selection and Genome-Wide Association Studies for Grain Protein Content Stability in a Nested Association Mapping Population of Wheat

Grain protein content (GPC) is controlled by complex genetic systems and their interactions and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by the environment. Thus, understanding the genetic control of wheat GPC and identifying genotypes with improved stability is an important breeding goal. The objectives of this research were to identify genetic backgrounds with less variation for GPC across environments and identify quantitative trait loci (QTLs) controlling the stability of GPC. A spring wheat nested association mapping (NAM) population of 650 recombinant inbred lines (RIL) derived from 26 diverse founder parents crossed to one common parent, ‘Berkut’, was phenotyped over three years of field trials (2014–2016). Genomic selection models were developed and compared based on predictions of GPC and GPC stability. After observing variable genetic control of GPC within the NAM population, seven RIL families displaying reduced marker-by-environment interaction were selected based on a stability index derived from a Finlay–Wilkinson regression. A genome-wide association study identified eighteen significant QTLs for GPC stability with a Bonferroni-adjusted p-value < 0.05 using four different models and out of these eighteen QTLs eight were identified by two or more GWAS models simultaneously. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased estimates reached up to r = 0.69. Genomic selection can be used to apply selection pressure for GPC and improve genetic gain for GPC.

Prediction of grain protein content and gluten quality of bread wheat in the early vegetation period by optical sensors

Estimation of the end-use quality characteristics of bread wheat in the early phenological period and before harvest is critically important for the flour industry. This study investigated the usability of the Normalized Difference Vegetation Index (NDVI) and Chlorophyll Meter (CM) values obtained by GreenSeeker and Chlorophyll Meter for prediction of protein content and gluten quality in the early growth stage of bread wheat. The results demonstrated that protein content was positively correlated with NDVI and CM values of the optical sensors, which can be predicted by using the equation developed with NDVI Zadoks (ZD)3.1 period values (R2 = 0.92, p <0.01). Compared to the NDVI values, higher correlated equations were obtained with the CM for the sedimentation volumes. Both sensor readings showed strong correlation with the GlutoPeak GSI (Gluten Strength Index) and GRI (Gluten Rheological Index) values in the ZD3.1 period. Also, while Cu, Fe and Mn contents of the grain tended to increase with increasing nitrogen doses according to both optical sensor readings, Zn content was inversely proportional to the NDVI and CM readings. The results of this study reveal that the grain protein content and gluten quality of bread wheat can be estimated successfully in the ZD3.1 period.

Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat.

Genomics and high throughput phenomics have the potential to revolutionize the field of wheat (Triticum aestivum L.) breeding. Genomic selection (GS) has been used for predicting various quantitative traits in wheat, especially grain yield. However, there are few GS studies for grain protein content (GPC), which is a crucial quality determinant. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. The objectives of this research were to compare performance of single and multi-trait GS models for predicting GPC and grain yield in wheat and to identify optimal growth stages for collecting secondary traits. We used 650 recombinant inbred lines from a spring wheat nested association mapping (NAM) population. The population was phenotyped over 3 years (2014–2016), and spectral information was collected at heading and grain filling stages. The ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12% for GPC and 20% for grain yield by including secondary traits in the models. Spectral information collected at heading was superior for predicting GPC, whereas grain yield was more accurately predicted during the grain filling stage. Green normalized difference vegetation index had the largest effect on the prediction of GPC either used individually or with multiple indices in the GS models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.

パン用コムギ「せときらら」の収量と開花期追肥量による子実タンパク質含有率の予測

パン用コムギ「せときらら」の収量と開花期追肥量による子実タンパク質含有率の予測

Prediction of grain protein content in winter wheat ( Triticum aestivum L.) using plant pigment ratio (PPR)

The applicability of the hyperspectral data from the canopy to the prediction of wheat grain quality was assessed for winter wheat. A training experiment and a validation experiment with contrasting nitrogen (N) levels and different cultivars were conducted, respectively, at different locations in Beijing, China. The wheat canopy spectral reflectance over 350–2500 nm, leaf N concentration and chlorophyll (Chl) concentration were measured at different growth stages, and the grain protein content was also determined after harvest. Eight vegetation indices (VIs) were compared relating to leaf N concentration, and the result indicated that the plant pigment ratio (PPR, (R550−R450)/(R550+R450)), a Chl-based index, was most applicable to predict wheat grain protein due to its significant correlation with leaf N concentration at the post-anthesis stage. Based on the relationships among PPR, leaf Chl concentration, leaf N concentration, and grain protein content, the statistical prediction models of grain protein content for Zhongyou9507 (a hard winter wheat) and Jingdong8 (a semi-hard winter wheat) were developed. The root mean square error (RMSE) of the 18 DAA (days after anthesis) model of Zhongyou9507 was 0.175; those of the anthesis model and the 11 DAA model of Jingdong8 were 0.238 and 0.982, respectively. Taking both the precision and accuracy into account, the 18 DAA model of Zhongyou9507 and the anthesis model of Jingdong8 were recommended to predict grain protein content for each cultivar. The result demonstrated that PPR could be used to assess grain quality of winter wheat.

Predicting grain yield and protein content in winter wheat and spring barley using repeated canopy reflectance measurements and partial least squares regression

By providing both spatial and temporal information remote sensing may function as an important source of data for site-specific crop management. This technology has been used for nitrogen application strategies to obtain optimum yield and grain quality. Here, the objective was to use early repeated remotely sensed multi-spectral data to predict grain yield and quality for winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.). The crops were sown with two different seeding rates and a wide range of nitrogen strategies were applied. Multi-way partial least squares regression (N-PLS) was used to predict grain yield and protein content. The results were compared with unfold-PLS1 and PLS1 using reflectance data from the last measurement day. Both single reflectance wavelengths and selected vegetation indices were used simultaneously. The results reveal that all models can make a good prediction of yield in both crops with unfold-PLS1 and N-PLS as the best. However, estimation of grain protein content at harvest was very poorly determined in barley, as no relation between the reflectance measurements and barley protein content was obtained. The relation between reflectance measurements and protein content was slightly better in wheat, where especially N-PLS improved the prediction of grain protein content. The overall conclusion of the present experiments is that data from repeated measurements of reflectance used in multi-way partial least squares regression before heading improved the prediction of grain yield and protein content in wheat and barley.

Chlorophyll Meter Readings in Winter Wheat: Cultivar Differences and Prediction of Grain Protein Content

Leaves of winter wheat (Triticum aestivum L.) were measured with a Hydro N-Tester (HNT), a portable chlorophyll meter, to obtain a relative estimate of leaf chlorophyll content, which gives an indication of plant N-status. The aim was to find if there were differences in HNT values between the winter wheat cultivars investigated, and to assess whether these values could be used for prediction of grain protein content (GPC). In Experiment 1, chlorophyll meter readings were taken at growth stages (GS) Zadoks 31 and 55 in five winter wheat (Triticum aestivum L.) cultivars in 17 annual field trials located in southeast Norway in 1996, 1997, and 1998. There were significant differences in HNT values between cultivars at both GS 31 and 55, but no differences were found between the three most commonly used cultivars in Norway at present, cvs. Bjørke, Mjølner and Rudolf. Because of the variation between years the HNT values at GS 55 were found to be unsuitable for direct prediction of the GPC. In Experiment 2, HNT was used in 21 annual N-fertilizer trials with winter wheat, performed in 1996 and 1997 and located in southeast Norway. A delay in the measurements from GS 55 to GS 65 generally improved the GPC prediction in 1996. Nitrogen applied at GS 55 increased GPC by approximately 1 unit of percent in cv. Folke, regardless of HNT value, while in cv. Portal nitrogen applied at this stage gave no increase in GPC at HNT values beyond 700.

Prediction of grain protein content in winter wheat through leaf color measurements using a chlorophyll meter

A field trial was conducted over a 3-year period at the Hokkaido Kitami Agricultural Experiment Station to examine whether the grain protein content (GPC) of a winter wheat cultivar (Triticum aestivum L. cv. Chihokukomugi) suitable for Japanese noodle-making could be predicted before harvest. The prediction of the GPC was accurate based on the color of the second leaf (just below the flag leaf) at the end of the emergence of the inflorescence, when nitrogen application was graded. In order to evaluate the reliability of this test, a survey of 95 wheat fields in the eastern part of Hokkaido was also carried out during a 3-year period. The prediction of the GPC for this cultivar based on the color of the second leaf was less accurate across many sites. The results of this survey, however, suggested that the leaf color could be used as an index for ranking the GPC as low or high in relation to processing requirements. When the leaf color value of the second leaf measured with a chlorophyll meter at the end of the emergence of the inflorescence was less than 40, it was predicted that the GPC would be lower than the processing requirement. This index could be applied to the cultivars grown in the eastern part of Hokkaido, except for those grown on peat soils.