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At least 73 records · Page 4

Connecting agriculture and renewable energy: insights into microclimatic changes, physiological, biochemical, and yield responses under agrivoltaics: a review

Agrivoltaics, the synergistic integration of agriculture and solar energy production on the same piece of land, has emerged as a compelling dual-use solution that maximizes land productivity while simultaneously addressing the need for sustainable agricultural practices and renewable energy generation. Despite the growing global interest in this dual-use system, the microclimatic shifts created beneath solar panels and their consequences for crop performance remain insufficiently synthesized. This review highlights the intricate interactions between agrivoltaics systems and plant microclimates, discussing their impacts on various physiological processes, metabolic pathways, and overall yield responses in different crop species. Evidence indicates that moderated light intensity and altered microclimates can enhance water-use efficiency, stabilize photosynthetic function, and trigger beneficial metabolic adjustments; however, responses remain highly species-specific and strongly dependent on regional climate conditions and panel configuration. Yield outcomes vary widely among vegetables, cereals, pulses, and fruit crops, highlighting the necessity for tailored agronomic strategies and crop selection within agrivoltaic designs. A critical knowledge gap identified in this review concerns the limited understanding of molecular and omics-level responses underlying plant adaptation to agrivoltaic environments. We further provide a detailed and interdisciplinary overview of adaptive agronomic strategies, and optimal crop selection, tailored to agrivoltaic systems. Despite the benefits of land use efficiency and simultaneous food and energy production, challenges remain concerning initial investment, technological adaptation, social and legal barriers, and shade-induced yield penalties. Further research in this area will be critical to enhancing the agricultural, environmental, and economic sustainability of agrivoltaics while simultaneously augmenting their practical utility and appeal to farmers in the future.

14 SOLAR ENERGY↗

Assessing Multi-Dimensional Impacts of Achieving Sustainability Goals by Projecting the Sustainable Agriculture Matrix Into the Future

The concept of sustainability inherently spans multiple spatial scales, sectors, variables, and time horizons. This study links a recently developed method of assessing present-day agricultural sustainability across environmental, economic, and social dimensions with a process-based integrated assessment model, in order to allow forward-looking analysis of sustainability by region and scenario. The sustainable agriculture matrix estimates present-day agricultural sustainability at the national level using 18 indicator variables, of which this study estimates nine to the year 2100, using an enhanced version of the Global Change Analysis Model. Scenarios include a reference scenario, and scenarios that apply the following measures, both individually and in combination, that are thought to improve sustainability: yield intensification, transition toward more plant-based (“flexitarian”) diets, and economy-wide greenhouse gas emissions mitigation. The scenarios illustrate considerable complexity and tradeoffs inherent to efforts to improve agricultural sustainability in all regions globally. For example, yield intensification typically increases nitrogen pollution, flexitarian diets can reduce agricultural output, and greenhouse gas mitigation efforts may either increase deforestation or crowd out crop and livestock production due to consequent bioenergy demands. However, there is considerable inter-regional heterogeneity in the responses, and the importance of such secondary responses also differs by region. The analysis and post-processing methods developed in this study allow quantification and visualization of the absolute and relative magnitude of the tradeoffs between agricultural sustainability indicator variables across regions, time periods, and scenarios.

54 ENVIRONMENTAL SCIENCES↗

The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems

Abstract As global land surface temperature continues to rise and heatwave events increase in frequency, duration, and/or intensity, our key food and fuel cropping systems will likely face increased heat-related stress. A large volume of literature exists on exploring measured and modelled impacts of rising temperature on crop photosynthesis, from enzymatic responses within the leaf up to larger ecosystem-scale responses that reflect seasonal and interannual crop responses to heat. This review discusses (i) how crop photosynthesis changes with temperature at the enzymatic scale within the leaf; (ii) how stomata and plant transport systems are affected by temperature; (iii) what features make a plant susceptible or tolerant to elevated temperature and heat stress; and (iv) how these temperature and heat effects compound at the ecosystem scale to affect crop yields. Throughout the review, we identify current advancements and future research trajectories that are needed to make our cropping systems more resilient to rising temperature and heat stress, which are both projected to occur due to current global fossil fuel emissions.

59 BASIC BIOLOGICAL SCIENCES↗

Data‐driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates

Summary Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well‐studied major crops. Using 727 maize ( Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre‐ and postanthesis maximum temperature, mid‐season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well‐studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates.

Kusmec, Aaron↗

Biochar as a carbon dioxide removal strategy in integrated long-run mitigation scenarios

Abstract Limiting global warming to under 2 °C would require stringent mitigation and likely additional carbon dioxide removal (CDR) to compensate for otherwise unabated emissions. Because of its technology readiness, relatively low cost, and potential co-benefits, the application of biochar to soils could be an effective CDR strategy. We use the Global Change Analysis Model, a global multisector model, to analyze biochar deployment in the context of energy system uses of biomass with CDR under different carbon price trajectories. We find that biochar can create an annual sink of up to 2.8 GtCO 2 per year, reducing global mean temperature increases by an additional 0.5%–1.8% across scenarios by 2100 for a given carbon price path. In our scenarios, biochar’s deployment is dependent on potential crop yield gains and application rates, and the competition for resources with other CDR measures. We find that biochar can serve as a competitive CDR strategy, especially at lower carbon prices when bioenergy with carbon capture and storage is not yet economical.

54 ENVIRONMENTAL SCIENCES↗

Quantifying leaf symptoms of sorghum charcoal rot in images of field‐grown plants using deep neural networks

Abstract Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red‐green‐blue images of sorghum plants exhibiting symptoms of infection. EfficientNet‐B3 and a fully convolutional network emerged as the top‐performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet‐B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models’ validation scores increased linearly, and their inference time decreased exponentially. This trend could be attributed to larger patches containing more information, improving model performance, and fewer patches reducing the computational load, thus decreasing inference time. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a foundation for drone‐based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web‐based application where users can easily analyze their own images.

Gonzalez, Emmanuel M.↗

Life cycle analysis of dedicated energy crops for fuel production in the United States

Dedicated energy crops are promising feedstocks to make biofuels including jet fuels. This study applies life cycle analysis (LCA) to estimate direct well-to-wake (WTW) greenhouse gas (GHG) emissions (g CO 2 e/MJ) for jet fuel derived from five energy crops-biomass sorghum, miscanthus, switchgrass, poplar, and willow-via Fischer-Tropsch-to-Jet (FTJ) and Ethanol-to-Jet (ETJ) pathways. The WTW boundary includes direct emissions from biomass production, fuel production, and fuel combustion. The R&D GREET model is expanded to conduct the LCA, using national average biomass yields and farming inputs from the 2023 Billion-Ton Study. In addition, this study estimates emissions from market-mediated effects, including induced land-use change, induced other crop (non-feedstock) production changes, and induced livestock production changes using global economic and emissions factor models. On a per-dry U.S. ton basis, cultivation and harvest emissions are lowest for willow (51,565 g CO 2 e) and highest for biomass sorghum (104,488 g CO 2 e). Per-acre results show similarly high emissions for sorghum and lowest values for poplar and willow. Direct WTW emissions are substantially lower for FTJ (biomass sorghum: 5.5; miscanthus: 10.3; switchgrass: 11.7; poplar: 11.9; and willow: 8.7 g CO 2 e/MJ) than ETJ (33.2; 33.8; 34.8; 36.2; and 31.7 g CO 2 e/MJ, respectively). When market-mediated emissions are included, miscanthus exhibits the lowest total emissions across energy crop pathways. Although results are sensitive to modeling assumptions, they indicate that high-yielding perennial and woody crops, particularly when planted on marginal land, could significantly reduce WTW emissions for bio-jet fuels by combining low direct emissions with soil carbon gains and favorable market-mediated effects.

Billion-Ton Study↗

Growth and biochemical composition of the carrageenophyte Eucheumatopsis isiformis (Solieriaceae, Rhodophyta) under cultivation at different irradiance levels

Long-term production of a few species of Eucheuma and Kappaphycus from the Indo-Pacific Ocean supports the global carrageenan industry. The widespread use of vegetative propagation has resulted in low genetic diversity among farmed populations, making these crops increasingly vulnerable to diseases and environmental stressors, and resulting in declining yields and reduced biomass production over time. Insufficient efforts to develop and select new native cultivars, combined with the ecological risks posed by introducing nonindigenous species, underscore the urgent need to enhance cultivar diversification in eucheumatoid farming. Eucheumatopsis isiformis , a native carrageenophyte to the Atlantic Ocean, has potential for its cultivation in the Yucatán peninsula, Mexico. This study evaluated the growth and biochemical composition (pigments, elemental C and N, kappa/iota carrageenan ratio) of E. isiformis under different irradiance conditions in an indoor cultivation system. Specimens were collected in a subtidal population in Yucatán, Mexico, characterized, acclimatized to laboratory conditions, and exposed to five irradiance levels (71 ± 10.59 –1014 ± 65.85 µmol quanta m −2 s −1 ; 23.4 °C, salinity 33–34). Growth was measured weekly for six weeks, and elemental C and N content, pigment composition, and the kappa/iota-carrageenan ratio were evaluated. Growth and chromatic photoacclimation to light conditions were observed in all treatments. Highest growth rates (3.1–3.2% day −1 ) were registered between 238 ± 28.3 – 518 ± 44.3 µmol quanta m −2 s −1 , with a predominant iota-type carrageenan (0.97 ± 0.02) observed in all treatments. This study aims to provide guidelines for the successful development of the aquaculture of this carrageenophyte.

FTIR↗

Chapter 9.12 - Weather, Climatic and Ecological Impacts of Onshore Wind Farms

Wind power is an environmentally sustainable technology that is likely to be part of the solution to the climate change, air pollution, and energy security problems. Despite many positive benefits, the rapid development of wind power has raised concerns about some potential adverse environmental impacts. While converting wind?s kinetic energy into electricity, wind turbines (WTs) modify properties of the atmospheric boundary layer (ABL) including the vertical profiles and surface-atmosphere exchanges of energy, momentum, mass, moisture, and trace gases. Given the current installed capacity and the projected installation worldwide, wind farms (WFs) are likely becoming a major driver of manmade land use change on Earth. Hence, understanding WT-atmosphere-surface interactions and assessing potential environmental impacts of WFs are of significant scientific, societal and economic importance. Here we review our progress in assessing potential impacts of onshore wind power on weather, climate and vegetation activity. A consensus is emerging based on observations and modeling studies that WFs cause a local to regional warming effect, particularly at nighttime, while the impacts on precipitation, wind patterns, crop yields and vegetation activity are uncertain. The warming effect results simply from vertical heat redistribution within the ABL due to turbine-enhanced vertical turbulent mixing in the wakes. At the global scale, with a substantial installation of WFs, mesoscale and climate models predicted large regional changes but small global impacts on temperature, while the impacts on precipitation, clouds, wind patterns and large-scale circulation have large uncertainties and are region specific and scale dependent. Despite increasing number of research efforts, our assessment of potential WF impacts is still very limited. Although the WF impacts are mostly local and limited to the near-surface ABL, this is the layer where we live and plants grow. Hence, more studies are needed to improve our understanding of WT-atmosphere-surface interactions and our capability to model and project the weather, climatic and ecological impacts of large WFs.

atmospheric boundary layer↗

Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity

Abstract Earth is currently undergoing a global increase in atmospheric vapor pressure deficit (VPD), a trend which is expected to continue as climate warms. This phenomenon has been associated with productivity decreases in ecosystems and yield penalties in crops, with these losses attributed to photosynthetic limitations arising from decreased stomatal conductance. Such VPD increases, however, have occurred over decades, which raises the possibility that stomatal acclimation to VPD plays an important role in determining plant productivity under high VPD. Furthermore, evidence points to more far‐ranging and complex effects of elevated VPD on plant physiology, extending to the anatomical, biochemical, and developmental levels, which could vary substantially across species. Because these complex effects are typically not considered in modeling frameworks, we conducted a quantitative literature review documenting temperature‐independent VPD effects on 112 species and 59 traits and physiological variables, in order to develop an integrated and mechanistic physiological framework. We found that VPD increase reduced yield and primary productivity, an effect that was partially mediated by stomatal acclimation, and also linked with changes in leaf anatomy, nutrient, and hormonal status. The productivity decrease was also associated with negative effects on reproductive development, and changes in architecture and growth rates that could decrease the evaporative surface or minimize embolism risk. Cross‐species quantitative relationships were found between levels of VPD increase and trait responses, and we found differences across plant groups, indicating that future VPD impacts will depend on community assembly and crop functional diversity. Our analysis confirms predictions arising from the hydraulic corollary to Darcy's law, outlines a systemic physiological framework of plant responses to rising VPD, and provides recommendations for future research to better understand and mitigate VPD‐mediated climate change effects on ecosystems and agro‐systems.

54 ENVIRONMENTAL SCIENCES↗

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗

Factors affecting the production of sugarcane yield and sucrose accumulation: suggested potential biological solutions

Environmental stresses are the main constraints on agricultural productivity and food security worldwide. This issue is worsened by abrupt and severe changes in global climate. The formation of sugarcane yield and the accumulation of sucrose are significantly influenced by biotic and abiotic stresses. Understanding the biochemical, physiological, and environmental phenomena associated with these stresses is essential to increase crop production. This review explores the effect of environmental factors on sucrose content and sugarcane yield and highlights the negative effects of insufficient water supply, temperature fluctuations, insect pests, and diseases. This article also explains the mechanism of reactive oxygen species (ROS), the role of different metabolites under environmental stresses, and highlights the function of environmental stress-related resistance genes in sugarcane. This review further discusses sugarcane crop improvement approaches, with a focus on endophytic mechanism and consortium endophyte application in sugarcane plants. Endophytes are vital in plant defense; they produce bioactive molecules that act as biocontrol agents to enhance plant immune systems and modify environmental responses through interaction with plants. This review provides an overview of internal mechanisms to enhance sugarcane plant growth and environmental resistance and offers new ideas for improving sugarcane plant fitness and crop productivity.

Mehdi, Faisal↗

Trade can buffer climate-induced risks and volatilities in crop supply

Climate change is intensifying the frequency and severity of extreme events, posing challenges to food security. Corn, a staple crop for billions, is particularly vulnerable to heat stress, a primary driver of yield variability. While many studies have examined climate impact on average corn yields, little attention has been given to the climate impact on production volatility. This study investigates the future volatility and risks associated with global corn supply under climate change, evaluating the potential benefits of two key adaptation strategies: irrigation and market integration. A statistical model is employed to estimate corn yield response to heat stress and utilize NEX-GDDP-CMIP6 climate data to project future production volatility and risks of substantial yield losses. Three metrics are introduced to quantify these risks: Sigma (σ), the standard deviation of year-on-year yield change, which reflects overall yield volatility; Rho (ρ), the risk of substantial loss, defined as the probability of yield falling below a critical threshold; and Beta (β), a relative risk coefficient that captures the volatility of a region's corn production compared to the globally integrated market. The analysis reveals a concerning trend of increasing year-on-year yield volatility (σ) across most regions and climate models. This volatility increase is significant for key corn-producing regions like Brazil and the United States. While irrigated corn production exhibits a smaller rise in volatility, suggesting irrigation as a potential buffer against climate change impacts, it is not a sustainable option as it can cause groundwater depletion. On the other hand, global market integration reduces overall volatility and market risks significantly with less sustainability concerns. Furthermore, these findings highlight the importance of a multidimensional approach to adaptation in the food sector. While irrigation can benefit individual farmers, promoting global market integration offers a broader solution for fostering resilience and sustainability across the entire food system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Data for Sustainable Co-Production of Plant Lipids and Cellulosic Sugars from Transgenic Energycane at an Industrially Relevant Scale: A Proof of Concept for Alternative Feedstocks

Development of sustainable and scalable technologies to convert lignocellulosic biomass to biofuels is critical to achieving carbon neutrality. The potential of transgenic bioenergy crops as a renewable source of sugars and lipids has been demonstrated at bench-scale. However, scaling up these processes is important for holistic analysis. Here proof-of-concept for chemical-free hydrothermal pretreatment of transgenic energycane-oilcane line L13 at an industrially relevant scale to recover vegetative lipids along with cellulosic sugars is presented. Pilot-scale processing of 97 kg of transgenic energycane-oilcane L13 stems and high solids pretreatment of bagasse enhanced the recovery of cellulosic glucose and xylose by 5-fold as compared to untreated bagasse and helped in the enrichment of vegetative lipids in the biomass residues which allowed its recovery at the end of the bioprocess. Palmitic and oleic acids were the predominant fatty acids (FAs) extracted from stems and leaves. The processing did not affect lipid composition. The efficiency of lipid recovery from untreated biomass was 75.9% which improved to 88.7% upon pretreatment. The vegetative tissues of transgenic energycane-oilcane L13 contained 0.42 metric tons/hectare of lipids. Processing vegetative tissues yielded 0.38 metric tons/hectare of lipids. This approaches an oil yield similar to soybean (global average 0.44 metric tons/hectare) and is almost twice as high as the oil yield from sugarcane engineered to hyperaccumulate lipids (0.20 metric tons/hectare). The study suggests that further optimization by state-of-the-art metabolic engineering and biomass processing can establish transgenic bioenergy crops for commercial drop-in fuel production.

Biomass Analytics↗

Sustainable co-production of plant lipids and cellulosic sugars from transgenic energycane at an industrially relevant scale: A proof of concept for alternative feedstocks

Development of sustainable and scalable technologies to convert lignocellulosic biomass to biofuels is critical to achieving carbon neutrality. The potential of transgenic bioenergy crops as a renewable source of sugars and lipids has been demonstrated at bench-scale. However, scaling up these processes is important for holistic analysis. Here proof-of-concept for chemical-free hydrothermal pretreatment of transgenic energycane-oilcane line L13 at an industrially relevant scale to recover vegetative lipids along with cellulosic sugars is presented. Here, pilot-scale processing of 97 kg of transgenic energycane-oilcane L13 stems and high solids pretreatment of bagasse enhanced the recovery of cellulosic glucose and xylose by 5-fold as compared to untreated bagasse and helped in the enrichment of vegetative lipids in the biomass residues which allowed its recovery at the end of the bioprocess. Palmitic and oleic acids were the predominant fatty acids (FAs) extracted from stems and leaves. The processing did not affect lipid composition. The efficiency of lipid recovery from untreated biomass was 75.9% which improved to 88.7% upon pretreatment. The vegetative tissues of transgenic energycane-oilcane L13 contained 0.42 metric tons/hectare of lipids. Processing vegetative tissues yielded 0.38 metric tons/hectare of lipids. This approaches an oil yield similar to soybean (global average 0.44 metric tons/hectare) and is almost twice as high as the oil yield from sugarcane engineered to hyperaccumulate lipids (0.20 metric tons/hectare). The study suggests that further optimization by state-of-the-art metabolic engineering and biomass processing can establish transgenic bioenergy crops for commercial drop-in fuel production.

59 BASIC BIOLOGICAL SCIENCES↗

Biomass Resources and Emission Reduction Potential of Agricultural and Livestock Residues in Mainland China from 2013 to 2022

Controlling carbon emissions is a global goal, and China is actively implementing carbon reduction measures. As a major agricultural nation, China has considerable potential for developing agricultural residues as renewable and environmentally friendly biomass energy. In this study, we obtained data on crop yields, crop-to-grain ratios, and livestock excretion coefficients to calculate the biomass resources of agricultural and livestock residues in Chinese provinces from 2013 to 2022. Crop residue biomass resources showed a distribution pattern with higher levels in the north than in the south and the east than in the west. Henan and Heilongjiang provinces consistently had the highest resource levels, exceeding 35 million tons annually for 10 years. The biomass resources from livestock residues were relatively abundant in Sichuan, Henan, Yunnan, Shandong, Hunan, and Inner Mongolia. Inner Mongolia, Sichuan, Shandong, and Henan had the greatest potential for CO2 emission reductions, primarily located in regions abundant in biomass resources and with high traditional energy consumption levels. ArcGIS was used to apply natural break classification to categorize the potential for emission reductions from agricultural and livestock residues across China from 2013 to 2022 into five classes. Based on factors such as crop planting area and livestock numbers, the spatiotemporal distribution of factors influencing the quantity of biomass resources was examined using Geographically and Temporally Weighted Regression. A tailored and integrated approach should be used for biomass, and the development of biomass energy should be promoted through policy support and technological innovation.

Environmental Sciences & Ecology↗

Scaling plant responses to high temperature from cell to ecosystem

Plant survival and productivity decrease with exposure to less favourable environments. The negative effects of environmental factors such as water-deficit and salinity stress on vegetation are relatively well studied, primarily due to the large losses in crop yield and forest biomass caused by these stresses. Although the impact of combined heat and drought on plants has received considerable interest in recent years due to climate change, there has been less focus on ascertaining the impact of heat stress alone at a range of temporal and spatial scales. The steady increase in global mean temperatures and (more importantly) variability in extreme temperatures is translating into more frequent and extreme heat stress episodes, which in turn reduce plant productivity. Further, projected increases in both day and night temperatures and the predicted impact of these changes on agricultural and forest productivity have drawn attention to the need to better address the direct effects of heat on plants. In particular, there has been tremendous interest in understanding the short- and long-term effects of heat at the cellular and whole-plant levels, including work on crops of agricultural importance and species from less managed ecosystems. Hence, we made a concerted effort in this Special Issue to capture recent progress in our ability to quantify the impact of heat stress, and identify new tools and approaches that have been developed and implemented to capture a wide range of responses in plants. The series of reviews and research articles presented here captures our current understanding of plant physiological and molecular responses to heat stress, as well as covering phenotyping methods, genetic diversity and potential routes through which plants can acclimate and adapt to warming climates.

54 ENVIRONMENTAL SCIENCES↗