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Abstract Warm temperatures due to increases of greenhouse gas emissions have changed temperature distribution patterns especially for their extremes, which negatively affect crop yields. However, the assessment of these negative impacts remains unclear when surface precipitation patterns are shifted. Using a statistical model along with 23,944 county-year maize-yield data during 1981–2020 in the US Corn Belt, we found that the occurrence of timely precipitation reduced the sensitivity of maize yields to extreme heat by an average of 20% during the growing season with variations across phenological periods. Spatially across the US corn belt, maize in the northern region exhibited more significant benefits from timely precipitation compared to the southern region, despite the pronounced negative effects of extreme heat on yields in cooler regions. This study underscores the necessity of incorporating timely precipitation as a pivotal factor in estimating heat effects under evolving climates, offering valuable insights into complex climate-related challenges. 

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. 

To better quantify methane emissions resulting from grazing cattle, a controlled methane release at an agricultural site is performed using dual comb spectroscopy. The achieved methane concentration precision is below 10 nmol/mol. The Author(s) Work of the US Government and not subject to copyright. 

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. 

Advances in spectroscopy have the potential to improve our understanding of agricultural processes and associated trace gas emissions. We implement field-deployed, open-path dual-comb spectroscopy (DCS) for precise multispecies emissions estimation from livestock. With broad atmospheric dual-comb spectra, we interrogate upwind and downwind paths from pens containing approximately 300 head of cattle, providing time-resolved concentration enhancements and fluxes of CH 4 , NH 3 , CO 2 , and H 2 O. The methane fluxes determined from DCS data and fluxes obtained with a colocated closed-path cavity ring-down spectroscopy gas analyzer agree to within 6%. The NH 3 concentration retrievals have sensitivity of 10 parts per billion and yield corresponding NH3 fluxes with a statistical precision of 8% and low systematic uncertainty. Open-path DCS offers accurate multispecies agricultural gas flux quantification without external calibration and is easily extended to larger agricultural systems where point-sampling-based approaches are insufficient, presenting opportunities for field-scale biogeochemical studies and ecological monitoring. 

Contrary to previous assumptions that most mutations are deleterious, there is increasing evidence for persistence of large-effect mutations in natural populations. A possible explanation for these observations is that mutant phenotypes and fitness may depend upon the specific environmental conditions to which a mutant is exposed. Here, we tested this hypothesis by growing large-effect flowering time mutants of Arabidopsis thaliana in multiple field sites and seasons to quantify their fitness effects in realistic natural conditions. By constructing environment-specific fitness landscapes based on flowering time and branching architecture, we observed that a subset of mutations increased fitness, but only in specific environments. These mutations increased fitness via different paths: through shifting flowering time, branching, or both. Branching was under stronger selection, but flowering time was more genetically variable, pointing to the importance of indirect selection on mutations through their pleiotropic effects on multiple phenotypes. Finally, mutations in hub genes with greater connectedness in their regulatory networks had greater effects on both phenotypes and fitness. Together, these findings indicate that large-effect mutations may persist in populations because they influence traits that are adaptive only under specific environmental conditions. Understanding their evolutionary dynamics therefore requires measuring their effects in multiple natural environments.