Scientific selection: A century of increasing crop varietal diversity in US wheat.

A prevalent and persistent biodiversity concern is that modern cropping systems lead to an erosion in crop genetic diversity. Although certain trait uniformity provides advantages in crop management and marketing, farmers facing risks from change in climate, pests, and markets are also incentivized to adopt new varieties to address complex and spatially variable genetics, environment, and crop management interactions to optimize crop performance. In this study, we applied phylogenetically blind and phylogenetically informed diversity metrics to reveal significant increases in both the spatial and temporal diversity of the US wheat crop over the past century. Contrary to commonly held perceptions on the negative impact of modern cropping systems on crop genetic diversity, our results demonstrated a win-win outcome where the widespread uptake of scientifically selected varieties increased both crop production and crop diversity.

Crop variety management for climate adaptation supported by citizen science.

Crop adaptation to climate change requires accelerated crop variety introduction accompanied by recommendations to help farmers match the best variety with their field contexts. Existing approaches to generate these recommendations lack scalability and predictivity in marginal production environments. We tested if crowdsourced citizen science can address this challenge, producing empirical data across geographic space that, in aggregate, can characterize varietal climatic responses. We present the results of 12,409 farmer-managed experimental plots of common bean (Phaseolus vulgaris L.) in Nicaragua, durum wheat (Triticum durum Desf.) in Ethiopia, and bread wheat (Triticum aestivum L.) in India. Farmers collaborated as citizen scientists, each ranking the performance of three varieties randomly assigned from a larger set. We show that the approach can register known specific effects of climate variation on varietal performance. The prediction of variety performance from seasonal climatic variables was generalizable across growing seasons. We show that these analyses can improve variety recommendations in four aspects: reduction of climate bias, incorporation of seasonal climate forecasts, risk analysis, and geographic extrapolation. Variety recommendations derived from the citizen science trials led to important differences with previous recommendations.

Efficient residual network using hyperspectral images for corn variety identification.

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.

The dynamics of N6-methyladenine RNA modification in resistant and susceptible rice varieties responding to rice stem borer damage.

N6-methyladenosine (m6A) is the most prevalent modification in cellular RNA which orchestrates diverse physiological and pathological processes during stress response. However, the differential m6A modifications that cope with herbivore stress in resistant and susceptible crop varieties remain unclear. Here, we found that rice stem borer (RSB) larvae grew better on indica rice (e.g., MH63, IR64, Nanjing 11) than on japonica rice varieties (e.g., Nipponbare, Zhonghua 11, Xiushui 11). Then, transcriptome-wide m6A profiling of representative resistant (Nipponbare) and susceptible (MH63) rice varieties were performed using a nanopore direct RNA sequencing approach, to reveal variety-specific m6A modifications against RSB. Upon RSB infestation, m6A methylation occurred in actively expressed genes in Nipponbare and MH63, but the number of methylation sites decreased across rice chromosomes. Integrative analysis showed that m6A methylation levels were closely associated with transcriptional regulation. Genes involved in herbivorous resistance related to mitogen-activated protein kinase, jasmonic acid (JA), and terpenoid biosynthesis pathways, as well as JA-mediated trypsin protease inhibitors, were heavily methylated by m6A, and their expression was more pronounced in RSB-infested Nipponbare than in RSB-infested MH63, which may have contributed to RSB resistance in Nipponbare. Therefore, dynamics of m6A modifications act as the main regulatory strategy for expression of genes involved in plant-insect interactions, which is attributed to differential responses of resistant and susceptible rice varieties to RSB infestation. These findings could contribute to developing molecular breeding strategies for controlling herbivorous pests.

A variety test platform for the standardization and data quality improvement of crop variety tests.

Variety testing is an indispensable and essential step in the process of creating new improved varieties from breeding to adoption. The performance of the varieties can be compared and evaluated based on multi-trait data from multi-location variety tests in multiple years. Although high-throughput phenotypic platforms have been used for observing some specific traits, manual phenotyping is still widely used. The efficient management of large amounts of data is still a significant problem for crop variety testing. This study reports a variety test platform (VTP) that was created to manage the whole workflow for the standardization and data quality improvement of crop variety testing. Through the VTP, the phenotype data of varieties can be integrated and reused based on standardized data elements and datasets. Moreover, the information support and automated functions for the whole testing workflow help users conduct tests efficiently through a series of functions such as test design, data acquisition and processing, and statistical analyses. The VTP has been applied to regional variety tests covering more than seven thousand locations across the whole country, and then a standardized and authoritative phenotypic database covering five crops has been generated. In addition, the VTP can be deployed on either privately or publicly available high-performance computing nodes so that test management and data analysis can be conveniently done using a web-based interface or mobile application. In this way, the system can provide variety test management services to more small and medium-sized breeding organizations, and ensures the mutual independence and security of test data. The application of VTP shows that the platform can make variety testing more efficient and can be used to generate a reliable database suitable for meta-analysis in multi-omics breeding and variety development projects.

Sweet Potato New Varieties Screening Based on Morphology, Pulp Color, Proximal Composition, and Total Dietary Fiber Content via Factor Analysis and Principal Component Analysis.

A sample set of 18 sweet potatoes [Ipomoea batatas (L.) Lam] segmented into six registered cultivars and 12 new varieties were evaluated. The 142 tuberous roots were obtained from a sweet potato germplasm bank (BAG-sweet potato; -27.417713768824555 and -49.64874168439556), specifically from plants belonging to a sweet potato breeding program. All samples were characterized according to their morphology, instrumental pulp color, proximate composition, and total dietary fiber. The analytical results were submitted to parametric and non-parametric statistical tests for sample variance data comparison. Moreover, the screening of the cultivars and new varieties was performed by exploratory statistical analysis, factor analysis (FA), and principal component analysis (PCA). From the sixteen independent variables that characterized the samples, the exploratory FA identified thirteen that had a communality greater than 0.7, with 92.08% of assertiveness. The PCA generated 4 principal components able to account for 84.01% of the explanatory variance. So, among the six registered cultivars, SCS372 Marina and SCS370 Luiza showed the capability to be employed as cultivars for production. Among the 12 sweet potato new varieties, samples 17025-13, 17125-10, and 17117 met the requirements for patent and registration. These results will be useful to farmers who wish to use these sweet potatoes in the development of their crops.

Effects of Agronomic Practices on Lygus spp. (Hemiptera: Miridae) Population Dynamics in Quinoa.

Crop diversification often promotes farm sustainability. However, proper management of newly introduced crops is difficult when pests are unknown. Characterizing herbivore dynamics on new crops, and how they respond to agronomic factors, is crucial for integrated pest management. Here we explored factors affecting Lygus spp. (Hemiptera: Miridae) herbivores in quinoa crops of Washington State. Quinoa is a newly introduced crop for North America that has multiple varieties and a range of agronomic practices used for cultivation. Through arthropod surveys and discussions with growers, we determined that Lygus spp. was the most abundant insect herbivore and likely contributed to low quinoa yields in previous seasons. We assessed how different varieties (Pison and QQ74), irrigation regimes (present and not), and planting methods (direct-seeded and transplanted) affected Lygus population dynamics. Lygus phenology was correlated with timing of quinoa seed-set in July and August, corresponding to a period when quinoa is most susceptible to Lygus. Both irrigation and planting manipulations had significant effects on Lygus abundance. Irrigation reduced Lygus abundance compared with nonirrigated plots in 2018. Planting method had a significant effect on Lygus populations in both 2017 and 2018, but effects differed among years. Variety had a significant effect on Lygus abundance, but only in nonirrigated plots. Overall, our study shows that Lygus is a common insect herbivore in quinoa, and careful selection of variety, planting method, and irrigation regime may be key components of effective control in seasons where Lygus abundance is high.

Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects.

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

Estimation of the Optimal Number of Replicates in Crop Variety Trials.

Replicated multi-location yield trials are conducted every year in all regions throughout the world for all regionally important crops. Heritability, i.e., selection accuracy based on variety trials, improves with increased number of replicates. However, each replicate is associated with considerable cost. Therefore, it is important for crop variety trials to be optimally replicated. Based on the theory of quantitative genetics, functions that quantitatively define optimal replication on the single-trial basis and on multi-location trial basis were derived. The function on the single-trial basis often over-estimates the optimum number of replicates; it is the function on multi-location trial basis that is recommended for determining the optimal number of replicates. Applying the latter function to the yield data from the 2015-2019 Ottawa oat registration trials conducted both in Ontario and in other provinces of Canada led to the conclusion that a single replicate or two replicates would have sufficed under the current multi-location trial setup. This conclusion was empirically confirmed by comparing genotypic rankings based on all replicates with that on any two replicates. Use of two replicates can save 33-50% of field plots without affecting the selection efficacy.

Preventive effects of combinative natural foods produced by elite crop varieties rich in anticancer effects on N-nitrosodiethylamine-induced hepatocellular carcinoma in rats.

The World Cancer Research Fund International has released 32 anticancer effects (ACEs) that targeted every stage of cancer processes. Thus, we designed two formulas of natural food combination Diet I and Diet II, mainly produced by elite crop varieties rich in ACEs with different mixture ratios, and evaluated their cancer preventive effects on N-nitrosodiethylamine (NDEA)-induced hepatocarcinogenesis. After 20 weeks of dietary intervention, Diet I and Diet II reduced incidence, size, and number of hepatic nodules (p < 0.01) and prevented hepatic tumor formation in NDEA-induced hepatocarcinogenesis rats. Low-grade hepatic dysplasia incidence was 20% for Diet II and 40% for Diet I, and apparent hepatocellular carcinomas (HCC) rates were both 0, while 90% HCC in control diet treatment group (p < 0.01). Diet I and Diet II ameliorated abnormal liver function enzymes, reduced serum alpha fetal protein, tumor-specific growth factor, dickkopf-related protein 1, tumor necrosis factor-alpha and interleukin-6 levels, regulated hepatic phase I and II xenobiotic-metabolizing enzymes, enhanced antioxidant capacity, suppressed NDEA-initiated oxidative DNA damage, and induced apoptosis coupled to down-regulation of proinflammatory, invasion, and angiogenesis markers. Daily intake of combination diet produced from ACEs-rich elite crop varieties can effectively prevent or delay occurrence and development of NDEA-induced hepatocarcinogenesis in rats.