Improvements in self-pollinated crops rely on crosses between different genotypes. It has been suggested that the repeated use of \"the best\" genotypes may lead to the restriction of the genetic diversity of the crop. In wheat, the analysis of gliadin (storage protein) polymorphism has provided evidence that genetic diversity was high and stable throughout the 20th century. Moreover, a worldwide analysis of gliadin polymorphism shows that genetic diversity is structured spatially across countries and their regions. Therefore, the analysis of gliadin genotypes in a given grain sample can provide reliable information about the origin of grains in this sample. An unexpected finding is that many registered common wheat cultivars are genetically non-uniform and composed of authentic biotypes (genotypically related lines originated from the initial cross) in spite of current crop-registration rules that include a strict demand for each new cultivar to be genetically uniform (DUS rules). In summary, the results suggest that each cultivar is the fruit of joint effects of a breeder and of a region\'s environmental factors. We believe this finding will not be restricted to wheat and suggest there may be a need to re-evaluate relevant rules of cultivar registration for crop species in general.

Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy. To improve the ultrasonic sensor measurement accuracy, a four-year (2018-2021) field experiment was conducted on maize and wheat, and a measurement platform was developed. A series of single-factor experiments were conducted to investigate the significant factors affecting measurements, including the observation angle (0-60°), observation height (0.5-2.5 m), observation period (8:00-18:00), platform moving speed with respect to the crop (0-2.0 m min-1), planting density (0.2-1 time of standard planting density), and growth stage (maize from three-leaf to harvest period and wheat from regreening to maturity period). The results indicated that both the observation angle and planting density significantly affected the results of ultrasonic measurements (p-value< 0.05), whereas the effects of other factors on measurement accuracy were negligible (p-value > 0.05). Moreover, a double-input factor calibration model was constructed to assess canopy height under different years by utilizing the normalized difference vegetation index and ultrasonic measurements. The model was developed by employing the least-squares method, and ultrasonic measurement accuracy was significantly improved when integrating the measured value of canopy heights and the normalized difference vegetation index (NDVI). The maize measurement accuracy had a root mean squared error (RMSE) ranging from 81.4 mm to 93.6 mm, while the wheat measurement accuracy had an RMSE from 37.1 mm to 47.2 mm. The research results effectively combine stable and low-cost commercial sensors with ground-based agricultural machinery platforms, enabling efficient and non-destructive acquisition of crop height information.

This study evaluated muti-mycotoxins in 199 samples including processed infant foods and raw materials collected randomly from an infant food company and assessed their role in dietary exposure in infants and young children via probabilistic risk assessment. Approximately 79.6 % (74/93) of the processed infant foods and 65.1 % (69/106) of the raw materials were contaminated by mycotoxins, with a mean occurrence level of 3.66-321.8 µg/kg. Deoxynivalenol (DON) and tenuazonic acid (TeA) were the more prevalent mycotoxins detected, based on their higher frequencies and levels across samples. Co-occurrence of more than two mycotoxins was detected in 61.3 % (57/93) of the processed infant foods and 53.8 % (57/106) of the raw materials. Wheat flour and derived products (e.g., infant noodles and infant biscuits) were contaminated with higher contamination levels and a greater variety of mycotoxins than other samples (e.g., infant cereal and rice grains). The estimated daily exposure to OTA, DON, ZEN, and TEN was lower than the corresponding reference health-based guidance values, indicating acceptable health risks. However, the estimated dietary exposure to alternariol monomethyl ether (AME), alternariol (AOH), and tenuazonic acid (TeA) exceeded the corresponding thresholds of toxicological concern values, indicating potential dietary intake risks. Among the various samples, cereals and cereal-based infant foods emerged as the primary contributors to mycotoxin exposure. Further research is advised to address the uncertainties surrounding the toxicity associated with emerging Alternaria mycotoxins and to conduct cumulative risk assessments concerning multiple mycotoxin exposure in infants and young children.

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Nitrogen (N) is needed for plant growth and development and is the major limiting nutrient due to its higher demand in agricultural production globally. The use of N fertilizers has increased considerably in recent years to achieve higher cereal yields. High N inputs coupled with declining N use efficiency (NUE) result in the degradation of the environment. Plants have developed multidimensional strategies in response to changes in N availability in soil. These strategies include N stress-induced responses such as changes in gene expression patterns. Several N stress-induced genes and other regulatory factors, such as microRNAs (miRNAs), have been identified in different plant species, opening a new avenue of research in plant biology. This review presents a general overview of miRNA-mediated regulation of N response and NUE. Further, the in-silico target predictions and the predicted miRNA-gene network for nutrient metabolism/homeostasis in wheat provide novel insights. The information on N-regulated miRNAs and the differentially expressed target transcripts are necessary resources for genetic improvement of NUE by genome editing.

Wheat-dependent exercise-induced anaphylaxis is a rare but severe form of anaphylaxis that occurs after consuming wheat products and engaging in physical activity. A case study of a 30-year-old woman suffering from chronic urticaria for the last 5 years highlights the difficulty in diagnosing this condition, as specific triggers were not identified. A diagnostic study called MADx revealed a positive analysis for omega-5-gliadin, leading to a diagnosis of wheat-dependent exercise-induced anaphylaxis. Delayed diagnosis is a common issue, and it can be challenging to distinguish wheat-dependent exercise-induced anaphylaxis from other conditions with similar symptoms. The treatment involves avoiding wheat products and always carrying an epinephrine auto-injector. When evaluating patients with similar symptoms, healthcare providers should include wheat-dependent exercise-induced anaphylaxis in their differential diagnosis. Patients should be educated about the symptoms, triggers, and management to seek immediate medical attention in an emergency.

In differential gene expression data analysis, one objective is to identify groups of co-expressed genes from a large dataset in order to detect the association between such a group of genes and an experimental condition. This is often done through a clustering approach, such as k-means or bipartition hierarchical clustering, based on particular similarity measures in the grouping process. In such a dataset, the gene differential expression itself is an innate attribute that can be used in the feature extraction process. For example, in a dataset consisting of multiple treatments versus their controls, the expression of a gene in each treatment would have three possible behaviors, upregulated, downregulated, or unchanged. We present in this chapter, a differential expression feature extraction (DEFE) method by using a string consisting of three numerical values at each character to denote such behavior, i.e., 1 = up, 2 = down, and 0 = unchanged, which results in up to 3B differential expression patterns across all B comparisons. This approach has been successfully applied in many research projects, and among these, we demonstrate the strength of DEFE in a case study on RNA-sequencing (RNA-seq) data analysis of wheat challenged with the phytopathogenic fungus, Fusarium graminearum. Combinations of multiple schemes of DEFE patterns revealed groups of genes putatively associated with resistance or susceptibility to FHB.

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

This study tries to evaluate the returns to contract farming (CF) in the form of farm efficiency for both contract and non-contract wheat growers in Haryana, North India. Applying the data envelopment analysis and endogenous switching regression model on cross-sectional survey data from 754 wheat farmers, it finds that CF adopters are significantly more efficient than non-adopters. Further, it reveals that farmers who adopt CF would reduce their technical efficiency by 16% if they do not participate in it. But non-adopters would increase their technical efficiency by 12% if they adopt instead. This is attributed to CF provisions of higher quality inputs and improved production technology. However, results also indicate that a small percentage of farmers are dealing with some financial constraints, including delayed payment, high cost of inputs, and lack of timely access to financial assistance. This needs to be addressed adequately in order to enfold the smallholders in the ambit of contracting system.

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