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Abstract The Genome-to-Phenome (G2P) problem is one of the highest-priority challenges in applied biology. Ecophysiological crop models (ECM) and genomic prediction (GP) models are quantitative algorithms, which, when given information on a genotype and environment, can produce an accurate estimate of a phenotype of interest. In this article, we discuss how the GP algorithms can be used to estimate genotype-specific parameters (GSPs) in ECMs to develop robust prediction methods. In this approach, the numerical constants (GSPs) that ECMs use to distinguish and characterize crop cultivars/varieties are treated as quantitative traits to be predicted by genomic prediction models from underlying genetic information. In this article we provide information on which GP methods appear favorable for predicting different types of GSPs, such as vernalization sensitivity or potential radiation use efficiency. For each example GSP, we assess a number of GP methods in terms of their suitability using a set of three criteria grounded in genetic architecture, computational requirements, and the use of prior information. In general, we conclude that the most useful algorithms were dependent on both the nature of the particular GSP and the GP methods considered. 

Abstract Climate extremes cause significant winter wheat yield loss and can cause much greater impacts than single extremes in isolation when multiple extremes occur simultaneously. Here we show that compound hot-dry-windy events (HDW) significantly increased in the U.S. Great Plains from 1982 to 2020. These HDW events were the most impactful drivers for wheat yield loss, accounting for a 4% yield reduction per 10 h of HDW during heading to maturity. Current HDW trends are associated with yield reduction rates of up to 0.09 t ha⁻¹per decade and HDW variations are atmospheric-bridged with the Pacific Decadal Oscillation. We quantify the “yield shock”, which is spatially distributed, with the losses in severely HDW-affected areas, presumably the same areas affected by the Dust Bowl of the 1930s. Our findings indicate that compound HDW, which traditional risk assessments overlooked, have significant implications for the U.S. winter wheat production and beyond. 

Abstract Vernalization genes underlying dramatic differences in flowering time between spring wheat and winter wheat have been studied extensively, but little is known about genes that regulate subtler differences in flowering time among winter wheat cultivars, which account for approximately 75% of wheat grown worldwide. Here, we identify a gene encoding anO-linkedN-acetylglucosamine (O-GlcNAc) transferase (OGT) that differentiates heading date between winter wheat cultivars Duster and Billings. We clone thisTaOGT1gene from a quantitative trait locus (QTL) for heading date in a mapping population derived from these two bread wheat cultivars and analyzed in various environments. Transgenic complementation analysis shows that constitutive overexpression ofTaOGT1bfrom Billings accelerates the heading of transgenic Duster plants.TaOGT1 is able to transfer anO-GlcNAc group to wheat proteinTaGRP2. Our findings establish important roles forTaOGT1in winter wheat in adaptation to global warming in the future climate scenarios. 

To obtain more consistent measurements through the course of a wheat growing season, we conceived and designed an autonomous robotic platform that performs collision avoidance while navigating in crop rows using spatial artificial intelligence (AI). The main constraint the agronomists have is to not run over the wheat while driving. Accordingly, we have trained a spatial deep learning model that helps navigate the robot autonomously in the field while avoiding collisions with the wheat. To train this model, we used publicly available databases of prelabeled images of wheat, along with the images of wheat that we have collected in the field. We used the MobileNet single shot detector (SSD) as our deep learning model to detect wheat in the field. To increase the frame rate for real-time robot response to field environments, we trained MobileNet SSD on the wheat images and used a new stereo camera, the Luxonis Depth AI Camera. Together, the newly trained model and camera could achieve a frame rate of 18–23 frames per second (fps)—fast enough for the robot to process its surroundings once every 2–3 inches of driving. Once we knew the robot accurately detects its surroundings, we addressed the autonomous navigation of the robot. The new stereo camera allows the robot to determine its distance from the trained objects. In this work, we also developed a navigation and collision avoidance algorithm that utilizes this distance information to help the robot see its surroundings and maneuver in the field, thereby precisely avoiding collisions with the wheat crop. Extensive experiments were conducted to evaluate the performance of our proposed method. We also compared the quantitative results obtained by our proposed MobileNet SSD model with those of other state-of-the-art object detection models, such as the YOLO V5 and Faster region-based convolutional neural network (R-CNN) models. The detailed comparative analysis reveals the effectiveness of our method in terms of both model precision and inference speed.