Climate change reduces frost exposure for high-value California orchard crops

Losing ground: projections of climate-driven bloom shifts and their implications for the future of California's almond orchards

Climate change is expected to impact the spring phenology of perennial trees, potentially altering the suitability of land for their cultivation. In this study, we investigate the effects of climate change on the bloom timing of almond orchards, focusing on California, the world's leading region for almond production. By analyzing historical climatic data, employing a model that considers hourly temperatures and fall non-structural carbohydrates to predict bloom dates, and examining various Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios, we assess the potential impacts of climate shifts on plant phenology and, consequently, on land suitability for almond farming. Our findings reveal that, within the next 30 years, the land suitable for almond production will not undergo significant changes. However, under unchanged emission scenarios, the available land to support almond orchard farming could decline between 48 to 73% by the end of the century. This reduction corresponds with an early shift in bloom time from the average Day of Year (DOY) 64 observed over the past 40 years to a projected earlier bloom between DOY 28-33 by 2100. These results emphasize the critical role climate shifts have in shaping future land use strategies for almond production in Central Valley, California. Consequently, understanding and addressing these factors is essential for the sustainable management and preservation of agricultural land, ensuring long-term food security and economic stability in the face of a rapidly changing climate.

Disaster process-based spatiotemporal characteristics of apricot frost in the warm temperate zone (WTZ), China

Frost stress is a major environmental factor that limits apricot growth in the warm temperate zone (WTZ) of China, and is always triggered by extreme low temperature weather processes. In this study, the characteristics of the apricot frost processes f(D, Tcum), which were identified from historical disaster representation, were analyzed and apricot frost evaluation indicators were developed, thus facilitating the process-based assessment and spatiotemporal analysis of apricot frost processes. Periods of low temperature that persist for 1~2, 3, and ≥4 days (i.e., duration days, D) provide the initial identification indicator for light, moderate, and severe apricot frost. The threshold ranges for Tcum are 0~3.9, 9.2~12.0, and >16.2 for D values of 1~2, 3, and ≥4, respectively. The northwest of the WTZ is dominated by apricot frost, with approximately 80% of apricot frost being light, followed by moderate and severe. Regional apricot frost exhibited a significant decreasing trend over the last four decades. A total of 29.65% of stations, which were mainly located in the northwest and middle parts of the study region, detected an increasing trend in apricot frost. The results provide technical support for targeted apricot frost level detection, and the process-based spatiotemporal characteristics of apricot frost can provide basic information for the prevention and mitigation of apricot frost.

An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security

The agriculture sector provides the majority of food supplies, ensures food security, and promotes sustainable development. Due to recent climate changes as well as trends in human population growth and environmental degradation, the need for timely agricultural information continues to rise. This study analyzes and predicts the impacts of climate change on food security (FS). For 2002-2021, Landsat, MODIS satellite images and predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, soil moisture, groundwater quality, soil types, digital elevation model, slope, and aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based on the Google Earth Engine (GEE) to detect agricultural land (AL). A remote sensing-based approach combined with the analytical network process (ANP) model was used to identify frost-affected areas. We then analyzed the relationship between climatic, geospatial, and topographical variables and AL and frost-affected areas. We found negative correlations of - 0.80, - 0.58, - 0.43, and - 0.45 between AL and LST, evapotranspiration, cloud ratio, and soil salinity, respectively. There is a positive correlation between AL and precipitation, sunny days, soil moisture, and groundwater quality of 0.39, 0.25, 0.21, and 0.77, respectively. The correlation between frost-affected areas and LST, evapotranspiration, cloud ratio, elevation, slope, and aspect are 0.55, 0.40, 0.52, 0.35, 0.45, and 0.39. Frost-affected areas have negative correlations with precipitation, sunny day, and soil moisture of - 0.68, - 0.23, and - 0.38, respectively. Our findings show that the increase in LST, evapotranspiration, cloud ratio, and soil salinity is associated with the decrease in AL. Additionally, AL decreases with a decreasing in precipitation, sunny days, soil moisture, and groundwater quality. It was also found that as LST, evapotranspiration, cloud ratio, elevation, slope, and aspect increase, frost-affected areas increase as well. Furthermore, frost-affected areas increase when precipitation, sunny days, and soil moisture decrease. Finally, we predicted the FS threat for 2030, 2040, 2050, and 2060 using the CA-Markov method. According to the results, the AL will decrease by 0.36% from 2030 to 2060. Between 2030 and 2060, however, the area with very high frost-affected will increase by about 10.64%. In sum, this study accentuates the critical impacts of climate change on the FS in the region. Our findings and proposed methods could be helpful for researchers to model and quantify the climate change impacts on the FS in different regions and periods.

Potential abiotic stress targets for modern genetic manipulation

Research into crop yield and resilience has underpinned global food security, evident in yields tripling in the past 5 decades. The challenges that global agriculture now faces are not just to feed 10+ billion people within a generation, but to do so under a harsher, more variable, and less predictable climate, and in many cases with less water, more expensive inputs, and declining soil quality. The challenges of climate change are not simply to breed for a "hotter drier climate," but to enable resilience to floods and droughts and frosts and heat waves, possibly even within a single growing season. How well we prepare for the coming decades of climate variability will depend on our ability to modify current practices, innovate with novel breeding methods, and communicate and work with farming communities to ensure viability and profitability. Here we define how future climates will impact farming systems and growing seasons, thereby identifying the traits and practices needed and including exemplars being implemented and developed. Critically, this review will also consider societal perspectives and public engagement about emerging technologies for climate resilience, with participatory approaches presented as the best approach.

Climatic suitability projection for deciduous fruit tree cultivation in main producing regions of northern China under climate warming

China is the largest fruit producer and consumer market in the world. Understanding the growing conditions responses to climate change is the key to predict future site suitability of main cultivation areas for certain deciduous fruit trees. In this study, we used dynamic and growing degree day models driven by downscaled daily temperatures from 22 global climate models to project the effects of climate change on growing conditions for deciduous fruit trees under two representative concentration pathway (RCP) 4.5 and RCP8.5 scenarios over 2 future time periods (represented by central years 2050s and 2085s) in northern China. The results showed a general increase of available winter chill for all sites under RCP4.5 scenario, and the most dramatic increase in chill accumulation could reach up to 36.8% in northeast regions for RCP8.5. However, the forecasted chill will decrease by 6.4% in southeast stations under RCP8.5 by 2085s. Additionally, the increase rate of growing season heat showed spatially consistency, and the most pronounced increase was found in the RCP8.5 by 2085s. For the southwest station, median heat accumulation increased by 20.8% in the 2050s and 37.1% in the 2085s under RCP8.5. Similar increasing range could be found in the northeast station; the median growing season heat increased by 19.8% and 38.8% in the 2050s and 2085s under RCP8.5, respectively. Moreover, the date of last spring frost was expected to advance and the frequency of frost occurrences was projected to decline in the study area compared to the past. Overall, the present study improves understanding regarding site-specific characteristics of climatic suitability for deciduous fruit tree cultivation in main producing regions of northern China. The results could provide growers and decision-makers with theoretical evidence to take adaptive measure to ensure fruit production in future.

Agroclimatic Metrics for the Main Stone Fruit Producing Areas in Spain in Current and Future Climate Change Scenarios: Implications From an Adaptive Point of View

Stone fruit production has enormous economic importance in Spain. Cultivation locations for these fruit species (i.e., peach, apricot, plum, and sweet cherry) cover wide and climatically diverse geographical areas within the country. Climate change is already producing an increase in average temperatures with special intensity in certain areas like the Mediterranean ones. These changes lead to a decrease in the accumulated chill, which can have a profound impact on the phenology of Prunus species like stone fruits due to, e.g., difficulties to cover the chilling requirements to break endodormancy, the occurrence of late frost events, or abnormal early high temperatures. All these factors can severely affect fruit production and quality and therefore provoke very negative consequences from the socio-economic point of view in the incumbent regions. Thus, characterization of current cultivation areas in terms of agroclimatic variables (e.g., chill and heat accumulation and probabilities of frost and early abnormal heat events), based on data from 270 weather stations for the past 20 years, is carried out in this work to produce an informative picture of the current situation. Besides, future climatic projections from different global climate models (data retrieved from the Meteorological State Agency of Spain-AEMET) up to 2065 for two Representative Concentration Pathway scenarios (i.e., RCP4.5 and RCP8.5) are also analyzed. Using the current situation as a baseline and considering the future scenarios, information on the current and future adaptive suitability of the different species/cultivars to the different growing areas can be inferred. This information could be the basis of a decision support tool to help the different stakeholders to take optimal decisions regarding current and future stone fruit or other temperate species cultivation in Spain.

Estimation of Apple Flowering Frost Loss for Fruit Yield Based on Gridded Meteorological and Remote Sensing Data in Luochuan, Shaanxi Province, China

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost.

Temperate Fruit Trees under Climate Change: Challenges for Dormancy and Chilling Requirements in Warm Winter Regions

Adequate chill is of great importance for successful production of deciduous fruit trees. However, temperate fruit trees grown under tropical and subtropical regions may face insufficient winter chill, which has a crucial role in dormancy and productivity. The objective of this review is to discuss the challenges for dormancy and chilling requirements of temperate fruit trees, especially in warm winter regions, under climate change conditions. After defining climate change and dormancy, the effects of climate change on various parameters of temperate fruit trees are described. Then, dormancy breaking chemicals and organic compounds, as well as some aspects of the mechanism of dormancy breaking, are demonstrated. After this, the relationships between dormancy and chilling requirements are delineated and challenging aspects of chilling requirements in climate change conditions and in warm winter environments are demonstrated. Experts have sought to develop models for estimating chilling requirements and dormancy breaking in order to improve the adaption of temperate fruit trees under tropical and subtropical environments. Some of these models and their uses are described in the final section of this review. In conclusion, global warming has led to chill deficit during winter, which may become a limiting factor in the near future for the growth of temperate fruit trees in the tropics and subtropics. With the increasing rate of climate change, improvements in some managing tools (e.g., discovering new, more effective dormancy breaking organic compounds; breeding new, climate-smart cultivars in order to solve problems associated with dormancy and chilling requirements; and improving dormancy and chilling forecasting models) have the potential to solve the challenges of dormancy and chilling requirements for temperate fruit tree production in warm winter fruit tree growing regions.