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Honey bees and other pollinators are critical for food production and nutritional security but face multiple survival challenges. The effect of climate change on honey bee colony losses is only recently being explored. While correlations between higher winter temperatures and greater colony losses have been noted, the impacts of warmer autumn and winter temperatures on colony population dynamics and age structure as an underlying cause of reduced colony survival have not been examined. Focusing on the Pacific Northwest US, our objectives were to (a) quantify the effect of warmer autumns and winters on honey bee foraging activity, the age structure of the overwintering cluster, and spring colony losses, and (b) evaluate indoor cold storage as a management strategy to mitigate the negative impacts of climate change. We perform simulations using the VARROAPOP population dynamics model driven by future climate projections to address these objectives. Results indicate that expanding geographic areas will have warmer autumns and winters extending honey bee flight times. Our simulations support the hypothesis that late-season flight alters the overwintering colony age structure, skews the population towards older bees, and leads to greater risks of colony failure in the spring. Management intervention by moving colonies to cold storage facilities for overwintering has the potential to reduce honey bee colony losses. However, critical gaps remain in how to optimize winter management strategies to improve the survival of overwintering colonies in different locations and conditions. It is imperative that we bridge the gaps to sustain honey bees and the beekeeping industry and ensure food and nutritional security.

Climate change poses growing risks to global agriculture including perennial tree fruit such as apples that hold important nutritional, cultural, and economic value. This study quantifies historical trends in climate metrics affecting apple growth, production, and quality, which remain understudied. Utilizing the high-resolution gridMET dataset, we analyzed trends (1979–2022) in several key metrics across the U.S.—cold degree days, chill portions, last day of spring frost, growing degree days (GDD), extreme heat days (daily maximum temperature >34 °C), and warm nights (daily minimum temperatures >15 °C). We found significant trends across large parts of the U.S. in all metrics, with the spatial patterns consistent with pronounced warming across the western states in summer and winter. Yakima County, WA, Kent County, MI, Wayne County, NY—leading apple-producers—showed significant decreasing trends in cold degree days and increasing trends in GDD and warm fall nights. Yakima county, with over 48 870 acres of apple orchards, showed significant changes in five of the six metrics—earlier last day of spring frost, fewer cold degree days, increasing GDD over the overall growth period, and more extreme heat days and warm nights. These trends could negatively affect apple production by reducing the dormancy period, altering bloom timing, increasing sunburn risk, and diminishing apple appearance and quality. Large parts of the U.S. experience detrimental trends in multiple metrics simultaneously that indicate the potential for compounding negative impacts on the production and quality of apples and other tree fruit, emphasizing the need for developing and adopting adaptation strategies.

Seasonal forecasts, which look several months into the future, are currently underutilized in active decision-making, particularly for agricultural and natural resource management. This underutilization can be attributed to the absence of forecasts for decision-relevant variables at the required spatiotemporal resolution and at the time when the decisions are made and a perception of poor skill by decision-makers. Addressing these constraints, we quantified the skill of seasonal forecasts in informing two agricultural decisions with differing decision timeframes and influencer variables: (a) whether to apply fertilizer in fall or wait until spring based on expected winter temperatures, and (b) drought response, such as whether to lease water based on expectations of drought. We also looked into how early the forecast can be provided without significant degradation in skill. Currently, drought response decisions are typically formulated in April, utilizing drought forecasts issued in the same month, while fall fertilization decisions are generally made between August and September. There is growing interest among stakeholders in the availability of earlier forecasts to inform these critical choices. We utilized the North American multi-model ensemble (NMME) hindcasts for the time period 1982–2020 over the Pacific Northwest US (PNW) to obtain meteorological variables. Runoff was estimated via simulations of the coupled crop-hydrology VIC-CropSyst model. The skill assessment with the Heidke Skill Score (HSS) yielded promising outcomes in both decisions for the entire PNW region. Notably, NMME’s positive skill (median HSS of 30%) in predicting warmer winters identifies years when fertilizer application should be avoided to prevent fertilizer loss through mineralization (and associated costs). Similarly, there is skill in forecasting drought conditions in most irrigated watersheds for up to two months in advance of April, the current decision time. In conclusion, our findings affirm that contrary to the perception of low skill and resulting underutilization, current seasonal forecasts hold the potential to inform at least some key agricultural decisions.

Winter chill accumulation is critical for the productivity and profitability of perennial tree fruit systems. Several studies have quantified the impacts of global warming on chill accumulation in the warmer production regions of the world, where insufficient chill events occur and their frequency is increasing. In contrast, we focus on a region with relatively cold winters–the Pacific Northwest United States (PNW)–where insufficient chill events are currently absent, and quantify the potential for introduction of these risks under climate change. Our results show spatial variation within the PNW, with chill accumulation projected to increase in some areas but decrease in others. There was also spatiotemporal variation in the driving factors of changes to chill accumulation. Even with decreases in chill accumulation, there are likely minimal issues with insufficient chill accumulation. However, delayed chill accumulation in combination with advances in the onset of heat accumulation can potentially shift the region from one where spring phenology is primarily forcing-driven to one where interaction between chilling and forcing processes become important. These interactions might create production risks for varieties with high chill requirements, post mid-21st century under high emissions scenarios. Future work should focus on understanding, modeling, and projecting responses across these overlapping chilling and forcing processes. Additionally, given significant spatial differences across a relatively small geographic range, it is also critical to understand and model these dynamics at a local landscape resolution for regions such as the PNW.

Streamflow augmentation can support endangered fish during low‐flow periods. Irrigated agriculture being the largest out‐of‐stream consumptive use, there is potential to augment streamflow by temporarily leasing from agriculture. This potential has not been fully realized, perhaps due to a focus on leasing at the extensive margin where land is either irrigated or not. Leasing at the intensive‐margin—applying less water on the full land extent—has received limited attention but could be promising, especially related to short‐term pulse flows for fish. We address this with two questions: Can a concurrent short‐term pause of irrigation withdrawals meaningfully increase streamflow for fish during critical periods? What is the associated foregone crop production and revenue loss? We used the CropSyst model to simulate irrigation demands, yield impacts from a 15‐day pause, and resulting revenue reductions for three focal watersheds in eastern Washington State of the United States, and generated water supply curves that provide the marginal cost of augmentation. We evaluated the strategy's ability to bring flows to levels beneficial for fish for at least two consecutive days. Results indicate that a short‐term irrigation pause can provide meaningful levels of pulse‐flows if conducive conditions related to upstream crop mix, acreage, and augmentation needs are met. The mean cost from lost revenue across years from reduced yields ranged from $1 to $125/acre in scenarios where the targeted streamflow was achieved. This water leasing strategy has potential in some locations to represent a win‐win situation for agricultural and environmental stakeholders and warrants further exploration.

Communication theory suggests that interactive dialog rather than information transmission is necessary for climate change action, especially for complex systems like agriculture. Climate analogs—locations whose current climate is similar to a target location’s future climate—have garnered recent interest as transmitting more relatable information; however, they have unexplored potential in facilitating meaningful dialogs, and whether the way the analogs are developed could make a difference. We developed climate context-specific analogs based on agriculturally-relevant climate metrics for US specialty crop production, and explored their potential for facilitating dialogs on climate adaptation options. Over 80% of US specialty crop counties had acceptable US analogs for the mid-twenty-first century, especially in the West and Northeast which had greater similarities in the crops produced across target-analog pairs. Western counties generally had analogs to the south, and those in other regions had them to the west. A pilot dialog of target-analog pairs showed promise in eliciting actionable adaptation insights, indicating potential value in incorporating analog-driven dialogs more broadly in climate change communication.

The extent of single- and multi-cropping systems in any region, as well as potential changes to them, has consequences on food security and land- and water-resource use, raising important management questions. However, addressing these questions is limited by a lack of reliable data on multi-cropping practices at a high spatial resolution, especially in areas with high crop diversity. In this paper, we develop and apply a relatively low-cost and scalable method to identify double-cropping at the field scale using satellite (Landsat) imagery. The process combines machine learning methods with expert labeling. The process evaluates multiple machine learning methods, including an image classification of a time-series, trained on data where cropping intensity labels were created by experts who are familiar with regional production practices. We demonstrate the process by measuring double-cropping extent in a part of Washington State in the Pacific Northwest United States—an arid region with cold winters and hot summers with significant production of more than 60 distinct types of crops including hay, fruits, vegetables, and grains in irrigated settings. Our results indicate that the current state-of-the-art methods for identifying cropping intensity—which apply simpler rule-based thresholds on vegetation indices—do not work well in regions with a high crop diversity and likely significantly overestimate double-cropped extent. Multiple machine learning models were applied on Landsat-derived vegetation index time-series data and were able to perform better by capturing nuances that the simple rule-based approaches are unable to. In particular, our (image-based) deep learning model was able to capture nuances in this crop-diverse environment and achieve a high accuracy (96–99% overall accuracy and 83–93% producer accuracy for the double-cropped class with a standard error of less than 2.5%) while also identifying double-cropping in the right crop types and locations based on expert knowledge. Our expert labeling process worked well and has potential as a relatively low-cost, scalable approach for remote sensing applications. The product developed here is valuable for the long-term monitoring of double-cropped extent and for informing several policy questions related to food production and resource use.

Orchard systems have drastically changed over the last three decades to high-density plantings that prioritize light interception that is evenly distributed throughout the entire canopy. These conditions allow the production of fruit with a high red color that meets consumer demands for uniformly colored fruit without external disorders. However, these systems also expose a higher proportion of fruit to full-sunlight conditions. In many semi-arid apple production regions, summer temperatures often exceed thresholds for the development of fruit sunburn, which can lead to >10% fruit losses in some regions and some years. To combat this, growers and researchers use sunburn mitigation strategies such as shade netting and evaporative cooling, which bring a different set of potential fruit quality impacts. Often, there is a tradeoff between red color development and fruit sunburn, particularly for strategies that affect light intensity reaching the fruit surface. In this paper, we review agronomic and environmental factors leading to reductions in red color and increases in sunburn incidence, along with advancements in management practices that help mitigate these issues. Furthermore, we also identify gaps in knowledge on the influence climate change might have on the viability of some practices that either enhance red color or limit sunburn for apple orchards in semi-arid environments. There is a need for cost-effective management strategies that reduce losses to sunburn but do not inhibit red color development in bicolor apple cultivars.

Global warming and heat stress can adversely affect crop yields and quality. Earlier planting that shifts the growing season to cooler periods is a widely considered adaptation strategy in climate change literature. We ask: How effective is earlier planting in reducing high-temperature-exposure across growth stages? What are the associated temperature-exposure tradeoffs, and can historical conditions be matched? With US spring wheat as a case study, growth-stage-specific temperature exposure signatures are developed to estimate tradeoffs from earlier planting. While earlier planting does reduce exposure to critical and lethal high temperatures during reproductive stages, it fails to replicate historical production conditions. The Pacific Northwest is an exception, although tail-end growth stages may require management. Historically-equivalent planting windows narrow presenting logistical challenges. Therefore, while many climate-change assessments list earlier planting as an effective adaptation strategy, it may not be as effective when tradeoffs are considered, and consideration of other strategies will be important. Earlier planting in the USA as an adaptation strategy for spring wheat fails to replicate historical crop growth conditions across timeframes and climate scenarios, according to growth-stage-specific temperature thresholds and climate model projections.

Future changes in crop evapotranspiration (ETc) are of interest to water management stakeholders. However, long-term projections are complex and merit further investigation due to uncertainties in climate data, differential responses of crops to climate and elevated atmospheric CO2, and adaptive agricultural management. We conducted factor-control simulation experiments using the process-based CropSyst model and investigated the contribution of each of these factors. Five major irrigated crops in the Columbia Basin Project area of the USA Pacific Northwest were selected as a case study and fifteen general circulation models (GCM) under two representative concentration pathways (RCP) were used as the climate forcing. Results indicated a wide range in ETc change, depending on the time frame, crop type, planting dates, and CO2 assumptions. Under the 2090s RCP8.5 scenario, ETc changes were crop-specific: +14.3% (alfalfa), +8.1% (potato), −5.1% (dry bean), −8.1% (corn), and −12.5% (spring wheat). Future elevated CO2 concentrations decreased ETc for all crops while earlier planting increased ETc for all crops except spring wheat. Changes in reference ET (ETo) only partially explains changes in ETc because crop responses are an important modulating factor; therefore, caution must be exercised in interpreting ETo changes as a proxy for ETc changes.