Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Transcriptomic Analysis of Leaves from Two Maize Hybrids Under Heat Stress During the Early Generative Stage

BACKGROUND

High temperatures during the early generative stage significantly threaten maize productivity, yet the molecular basis of heat tolerance remains unclear.

METHODS

To elucidate the molecular mechanisms of heat tolerance in maize, two hybrids-ZD309 (heat-tolerant) and XY335 (heat-sensitive)-were selected for integrated transcriptomic and physiological analyses. The plants were subjected to high-temperature treatments (3-5 °C above ambient field temperature) for 0, 1, 3, 5, and 7 days, with controls grown under natural conditions. Physiological indices, including Superoxide dismutase (SOD) activity, and proline (PRO), malondialdehyde (MDA), soluble sugar, and protein content, were measured.

RESULTS

Transcriptome analysis identified 1595 differentially expressed genes (DEGs) in XY335 (509 up- and 1086 down-regulated) and 1526 DEGs in ZD309 (863 up- and 663 down-regulated), with the most pronounced changes occurring on day 5. Key DEGs in XY335 were enriched in galactose metabolism and carbohydrate catabolism, whereas ZD309 exhibited rapid activation of oxidative stress and cell wall integrity pathways. Mfuzz time-series analysis categorized DEGs from XY335 and ZD309 into six clusters each. Weighted gene co-expression network analysis (WGCNA) identified 10 hub genes involved in ubiquitin thioesterase activity and RNA modification, suggesting protein-level regulatory roles.

CONCLUSIONS

This study reveals distinct transcriptional dynamics between heat-tolerant and heat-sensitive varieties, providing candidate genes for breeding thermotolerant maize and advancing our understanding of heat stress responses during critical reproductive stages.

Epigenetic variation in maize agronomical traits for breeding and trait improvement

Epigenetics-mediated breeding (epibreeding) involves engineering crop traits and stress responses through the targeted manipulation of key epigenetic features to enhance agricultural productivity. While conventional breeding methods raise concerns about reduced genetic diversity, epibreeding propels crop improvement through epigenetic variations that regulate gene expression, ultimately impacting crop yield. Epigenetic regulation in crops encompasses various modes, including histone modification, DNA modification, RNA modification, non-coding RNA, and chromatin remodeling. This review summarizes the epigenetic mechanisms underlying major agronomic traits in maize and identifies candidate epigenetic landmarks in the maize breeding process. We propose a valuable strategy for improving maize yield through epibreeding, combining CRISPR/Cas-based epigenome editing technology and Synthetic Epigenetics (SynEpi). Finally, we discuss the challenges and opportunities associated with maize trait improvement through epibreeding.

KRN5b regulates maize kernel row number through mediating phosphoinositol signalling

Kernel row number (KRN) is a major yield related trait for maize (Zea mays L.) and is also a major goal of breeders, as it can increase the number of kernels per plant. Thus, identifying new genetic factors involving in KRN formation may accelerate improving yield-related traits genetically. We herein describe a new kernel number-related gene (KRN5b) identified from KRN QTL qKRN5b and encoding an inositol polyphosphate 5-phosphatase (5PTase). KRN5b has phosphatase activity towards PI(4,5)P2, PI(3,4,5)P3, and Ins(1,4,5)P3 in vitro. Knocking out KRN5b caused accumulation of PI(4,5)P2 and Ins(1,4,5)P3, resulting in disordered kernel rows and a decrease in the number of kernels and tassel branches. The introgression of the allele with higher expression abundance into different inbred lines could increase the ear weight of the inbred lines and the corresponding hybrids by 10.1%-12.2% via increasing KRN, with no adverse effects on other agronomic traits. Further analyses showed that KRN5b regulates inflorescence development through affecting the synthesis and distribution of hormones. Together, KRN5b contributes to spikelet pair meristem development through inositol phosphate and phosphatidylinositols, making it a selecting target for yield improvement.

Heat-stress-induced ROS in maize silks cause late pollen tube growth arrest and sterility

The reproductive phase of plants is highly sensitive to ambient temperature stresses. To investigate sensitivity of female reproductive organs in grass crops during the pollination phase, we exposed the elongated stigma (silk) of maize to ambient environment at the silking stage. Moderate heat stress causes cell death of silk hair cells but did not affect early pollen tube growth inside the silk. Late pollen tube growth arrest was observed, leading to sterility. Heat stress causes elevated levels of reactive oxygen species (ROS) in silks, whose levels can be reduced by scavengers partly restoring pollen tube growth and fertility. A number of biological processes including hydrogen peroxide catabolic processes and bHLH transcription factor genes are downregulated by heat stress, while some NAC transcription factor genes are strongly upregulated. In conclusion, this study now provides a basis to select genes for engineering heat-stress-tolerant grass crops during the pollination phase.

The additive function of YIGE2 and YIGE1 in regulating maize ear length

Ear length (EL) is a key trait that greatly contributes to yield in maize. Although dozens of EL quantitative trait loci have been mapped, very few causal genes have been cloned, and the molecular mechanisms remain largely unknown. Our previous study showed that YIGE1 is involved in sugar and auxin pathways to regulate ear inflorescence meristem (IM) development and thus affects EL in maize. Here, we reveal that YIGE2, the paralog of YIGE1, regulates maize ear development and EL through auxin pathway. Knockout of YIGE2 causes a significant decrease of auxin level, IM length, floret number, EL, and grain yield. yige1 yige2 double mutants had even shorter IM and ears implying that these two genes redundantly regulate IM development and EL. The genes controlling auxin levels are differential expressed in yige1 yige2 double mutants, leading to lower auxin level. These results elucidated the critical role of YIGE2 and the redundancy between YIGE2 and YIGE1 in maize ear development, providing a new genetic resource for maize yield improvement.

Thresholds and extent of temperature effects on maize yield differ in different grain-filling stages

Temperature is a vital environmental factor affecting grain filling and maize yield. The response of maize yield to temperature at different stages of grain filling, however, remains uncharacterized. This study used "Zhengdan 958" as the test material to analyze the high-temperature threshold and yield sensitivity of grain-filling in different periods without water stress by using the data from staging sowing experiments at agro-meteorological experimental stations in Hebi and Suzhou in the Huang-Huai-Hai Plain from 2019 to 2022. The results demonstrated that: (1) the maximum temperature threshold was different in various periods of maize grain-filling in the Huang-Huai-Hai Plain, showing the early grain-filling period (EP) > the active grain-filling period (AP) > the late grain-filling period (LP). With the largest differences in temperature thresholds found in AP, the maximum temperature threshold of AP can better reflect the characteristics of grain filling rather than the whole filling period. (2) The heat of the grain-filling period can explain more than 80 % of the yield variation and affect the yield by influencing the number of days required to reach the maximum grain-filling rate (Vmaxd) and the duration of the active grain-filling period (DAP). (3) The growing degree days (GDD) is the most significant controlling factor affecting yield; however, the effect of heat degree days (HDD) cannot be ignored. The HDD and cumulative thresholds of HDD in the EP and AP of grain-filling can better reflect the effect of heat on yield. The accumulation thresholds of HDD at Hebi and Suzhou were 28.1 °C·d and 15.2 °C·d in the EP period, and 31.0 °C·d and 14.9 °C·d in the AP period, respectively. The results provide a basis for the precise identification of heat disasters during grain-filling and the scientific adjustment of sowing dates.

Maize and heat stress: Physiological, genetic, and molecular insights

Global mean temperature is increasing at a rapid pace due to the rapid emission of greenhouse gases majorly from anthropogenic practices and predicted to rise up to 1.5°C above the pre-industrial level by the year 2050. The warming climate is affecting global crop production by altering biochemical, physiological, and metabolic processes resulting in poor growth, development, and reduced yield. Maize is susceptible to heat stress, particularly at the reproductive and early grain filling stages. Interestingly, heat stress impact on crops is closely regulated by associated environmental covariables such as humidity, vapor pressure deficit, soil moisture content, and solar radiation. Therefore, heat stress tolerance is considered as a complex trait, which requires multiple levels of regulations in plants. Exploring genetic diversity from landraces and wild accessions of maize is a promising approach to identify novel donors, traits, quantitative trait loci (QTLs), and genes, which can be introgressed into the elite cultivars. Indeed, genome wide association studies (GWAS) for mining of potential QTL(s) and dominant gene(s) is a major route of crop improvement. Conversely, mutation breeding is being utilized for generating variation in existing populations with narrow genetic background. Besides breeding approaches, augmented production of heat shock factors (HSFs) and heat shock proteins (HSPs) have been reported in transgenic maize to provide heat stress tolerance. Recent advancements in molecular techniques including clustered regularly interspaced short palindromic repeats (CRISPR) would expedite the process for developing thermotolerant maize genotypes.

Elevated CO2 ameliorates the high temperature stress effects on physio-biochemical, growth, yield traits of maize hybrids

The rising temperatures and levels of carbon dioxide in the atmosphere are anticipated to have a significant impact on the productivity of agricultural crops. Although, the individual effects of elevated CO2 and temperature have been extensively studied in C3 and C4 crops, there remains a scarcity of research investigating their interactive effects specifically on maize hybrids. The impact of elevated temperature and its interaction with elevated CO2 on phenology, physiology, biomass, and grain yield of maize hybrids was assessed in a field experiment using Free Air Temperature Elevation (FATE) facility. The results showed that elevated temperature (eT) increased the anthesis silking interval (ASI), while the presence of elevated CO2 along with elevated temperature (eT + eCO2) mitigated this effect. The differential expression were observed between hybrids depending on their genetic potential. Furthermore, the net photosynthetic rate (Anet), stomatal conductance (gs), and transpiration rate (Tr) of hybrids decreased under elevated temperature but eT + eCO2 condition helped in reverting its impact to some extent. In term of leaf composition, the highest level of total soluble sugars (TSS) and starch was observed under eT + eCO2 conditions, possibly due to improved Anet in the presence of elevated eCO2. The negative impact of eT was also evident through increased proline and MDA content, but eT + eCO2 ameliorated the adverse effect of eT. The biomass and grain yield also responded similarly, among the hybrids 900M GOLD recorded superior performance for grain yield at eT condition exceeding 35 °C. On the other hand, DHM117 experienced a significant reduction in grain yield under eT, but performed better under eT + eCO2 due to its improved physiological response to eCO2. The study indicated that elevated levels of carbon dioxide can actually mitigate the detrimental effects of elevated temperature on maize crop. This positive impact on maize crop can be attributed to an enhanced physiological performance in the presence of eCO2 which enables the plants to maintain satisfactory yield levels despite the challenging environmental conditions.

Impacts of climate change on cassava yield and lifecycle energy and greenhouse gas performance of cassava ethanol systems: An example from Guangxi Province, China

Significant shock of climate change on crop yield will challenge the performance of bio-crop on substituting fossil energy to mitigate climate change. Taking cassava-to-ethanol system in Guangxi Province of South China as an example, we coupled a random forest (RF) model with 10 Global climate models (GCMs) outputs to predict the future cassava yields. Subsequently, the net energy value (NEV) and greenhouse gas (GHG) emissions of the cassava-to-ethanol system across varied topographies are assessed using a life cycle analysis. We demonstrate that the abrupt increases in temperatures are the primary contributors to declining yields. Notably, cassava yields in hilly regions decline more than those in plains and display greater variability among concentration pathway scenarios over time. Future NEV and GHG performance of cassava-to-ethanol will undergo significant decreases over time, especially within the high concentration pathway scenario (NEV decrease 28%, GHG increase 3.4% from 2006 to 2100). The performance reductions in hilly area are exacerbated by more harvest loss and labor and material inputs during the "field-to-wheel", negating its energy advantage over fossil fuels. Therefore, adopting a lower concentration pathway and favoring plantation in plains could maintain cassava-to-ethanol as a viable climate mitigation strategy. Our research also advances the methodological approach to climate change adaptation within the domain of life cycle assessment.

The heat stress during anthesis and the grain-filling period of spring maize in Northeast China is projected to increase toward the mid-21st century

BACKGROUND

Against the background of global warming, heat stress has become more frequent, which adversely affects the growth and development of spring maize plants in Northeast China. To adapt the regional maize production to climate change, it is imperative to understand the spatio-temporal characteristics of heat stress. In the present study, we analyzed three of the indices for heat stress, including the number of heat stress days, heating degree days (HDD, the total heat degree-days during critical stages), and the percentage of stations with heat stress.

RESULTS

From 1981 to 2019, the number of heat stress days varied greatly among the study years, ranging from 0 to 14 and 27 days, respectively. The average HDD was 7.8 and 5.0 °C day from 1981 to 2000, respectively, and the main hot-spots of heat stress occurred in the southwest regions. Moreover, compared with 1981-2000, the region of HDD during anthesis higher than 10 °C day in 2041-2060 under the SSP1-2.6 and SSP5-8.5 climate scenarios increased by 9.1-50.1% and 0.1-28.6%. The average HDD during the critical stages from 2041 to 2060 increased under the SSP5-8.5 climate scenario, being 1.5 times higher than that during 1981-2000. HDD during maize anthesis and the grain-filling period showed an overall increasing trend with years. About 19% and 58% of the study locations showed heat stress during the past 39 years, respectively.

CONCLUSION

Heat stress during anthesis and the grain-filling period for spring maize in Northeast China is projected to increase toward the mid-21st century. © 2023 Society of Chemical Industry.

High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method

High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs.

Investigation of climate-quality relationship in bread wheat (T. aestivum L.) by novel statistical approach (ESOGÜ quality index)

In this study, precipitation; temperature (maximum, minimum, and average temperature) values of Eskişehir, Konya, Afyonkarahisar, Uşak, and Kütahya for years (2007-2018); and protein content, macro sedimentation (MSDS), thousand kernel weight (KW), test weight (TW) relations, and the effect of climate values on quality were investigated. The Kriging method was used by ArcGIS software for creating quality maps of Eskişehir, Konya, Afyonkarahisar, Uşak, and Kütahya provinces in the light of obtained data from these examined quality criteria, yield, and climate factors. The quality of bread wheat, which includes protein content, macro sedimentation, thousand kernel weight, and test weight, is highly affected by the subject precipitation, maximum temperature, minimum temperature, average temperature, and precipitation. While the months of November, March, and April and the total annual precipitations affect the quality, the most effective precipitation is the months of April and November. Again, the fact that the winter months are hot, especially in January and February, causes the plant to be inadequate to withstand the winter, causing the plant to be more affected by the low temperatures in the early spring and to reduce the quality due to insufficient plant growth. Climatic factors affect quality in total, not alone, but cumulatively. It was concluded that the best quality wheat can be obtained from Konya, Eskişehir, and Afyonkarahisar provinces. It was concluded that ESOGÜ quality index (EQI), evaluating and integrating protein content, macro sedimentation, thousand kernel weight, and test weigh together, can be used safely in bread wheat genotypes.

High emissions could increase the future risk of maize drought in China by 60-70

Drought events have considerable direct and indirect economic, environmental, and social impacts, but few studies have analyzed and assessed future changes in drought disasters from a risk perspective to guide responses and adaptations thoroughly. Studying the potential climate-related impacts on future crop yield is therefore urgently needed. Intercomparison of the three Shared Socio-economic Pathway (SSP) scenarios based drought risks and yield loss of China was carried out using the climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and the hotspots of high drought risk regions were identified. This study found that the areas affected by severe maize drought (loss ratio larger than 0.2) accounted for 16.13 %, 20.79 %, and 18.87 % of the total national corn areas under three low, medium-to-high and high emission scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) respectively. The northwest China maize region, the ecotone between agriculture and animal husbandry, and the western central northern China maize region have relatively high loss risk. Compared with SSP1-2.6, the yield loss rates increased with 70.73 % and 61.52 % of national corn areas for SSP3-7.0 and SSP5-8.5, respectively. There is a decrease in the areas with low-risk and a significant increase in the areas with high-risk for SSP3-7.0 and SSP5-8.5 compared to the SSP1-2.6. These results may provide theoretical support for agricultural drought risk reduction and adaptation planning to ensure food security under climate change.

An overview of the interactions between food production and climate change

This paper provides an overview of how food production influences climate change and also illustrates the impact of climate change on food production. To perform such an overview, the (inter)link between different parts of the food supply chain continuum (agriculture production, livestock farming, food processing, food transport and storing, retail food, and disposal of food waste) and climate change has been investigated through a bibliometric analysis. Besides UN Sustainable Development Goal (SDG) 13, associated with climate change, other SDGs that are associated with this overview are goals #1, #2, #3, #6, #7, #12, and #15. Based on the evidence gathered, the paper provides some recommendations that may assist in efforts to reduce the climate-related impacts of food production.

Building social resilience in North Korea can mitigate the impacts of climate change on food security

Adaptation based on social resilience is proposed as an effective measure to mitigate hunger and avoid food shocks caused by climate change. But these have not been investigated comprehensively in climate-sensitive regions. North Korea (NK) and its neighbours, South Korea and China, represent three economic levels that provide us with examples for examining climatic risk and quantifying the contribution of social resilience to rice production. Here our data-driven estimates show that climatic factors determined rice biomass changes in NK from 2000 to 2017, and climate extremes triggered reductions in production in 2000 and 2007. If no action is taken, NK will face a higher climatic risk (with continuous high-temperature heatwaves and precipitation extremes) by the 2080s under a high-emissions scenario, when rice biomass and production are expected to decrease by 20.2% and 14.4%, respectively, thereby potentially increasing hunger in NK. Social resilience (agricultural inputs and population development for South Korea; resource use for China) mitigated climate shocks in the past 20 years (2000-2019), even transforming adverse effects into benefits. However, this effect was not significant in NK. Moreover, the contribution of social resilience to food production in the undeveloped region (15.2%) was far below the contribution observed in the developed and developing regions (83.0% and 86.1%, respectively). These findings highlight the importance of social resilience to mitigate the adverse effects of climate change on food security and human hunger and provide necessary quantitative information.

Increased probability and severity of compound dry and hot growing seasons over world's major croplands

Dry and hot extremes are major sources of risk to crop yields, and their impacts are expected to increase under future global warming. The co-occurring dry and hot conditions during crop growing seasons have amplified impacts on crop health that are even larger than the sum of their individual impacts, which may cause crop failure. In this study, we focus on the compound dry and hot growing seasons (hereafter CDHGS) for global wheat, rice, maize and soybean in the period 1951-2020. Total precipitation (TP) and accumulated active temperature (AAT) are used as indicators of overall water stress and heat stress, respectively, at the growing season scale. A copula model is used to construct joint distributions of TP and AAT sequences to investigate the joint behavior of dry and hot conditions during crop growing seasons. Our results indicate that after 1980, the growing seasons of the four crops become drier and more rapidly hotter across the globe, the probability of extreme CDHGS (P(TP ≤ TP₂₅,AAT > AAT₇₅)) increases in more than 80% of global croplands, the severity of CDHGS increases in more than 83% of global croplands, especially in Europe, Central Africa and eastern China. This study provides a global dimension analysis on the changes in compound dry and hot stresses within crops growing seasons in the context of global warming, offering helpful techniques to study the interaction between multi-hazards that occur during crop growth processes, which can effectively contribute to guiding the decision-making processes related to risk reduction and agricultural practices.

Genetic variation in YIGE1 contributes to ear length and grain yield in maize

Ear length (EL), which is controlled by quantitative trait loci (QTLs), is an important component of grain yield and as such is a key target trait in maize breeding. However, very few EL QTLs have been cloned, and their molecular mechanisms are largely unknown. Here, using a genome wide association study (GWAS), we identified a QTL, YIGE1, which encodes an unknown protein that regulates EL by affecting pistillate floret number. Overexpression of YIGE1 increased female inflorescence meristem (IM) size, increased EL and kernel number per row (KNPR), and thus enhanced grain yield. By contrast, CRISPR/Cas9 knockout and Mutator insertion mutant lines of YIGE1 displayed decreased IM size and EL. A single-nucleotide polymorphism (SNP) located in the regulatory region of YIGE1 had a large effect on its promoter strength, which positively affected EL by increasing gene expression. Further analysis shows that YIGE1 may be involved in sugar and auxin signal pathways to regulate maize ear development, thus affecting IM activity and floret production in maize inflorescence morphogenesis. These findings provide new insights into ear development and will ultimately facilitate maize molecular breeding.

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach

Understanding the development mechanism of drought events, characterization of future drought metrics, and its impact on crop yield is crucial to ensure food security globally, and more importantly, in South Asia. Therefore, the present study assessed the changes in future projected drought metrics and evaluated the future risk of yield reduction under drought intensity. We characterized the magnitude, intensity, and duration of future drought by means of the SPEI drought index using CMIP6 (Coupled Model Inter-comparison Phase-6) climate models. The impact of future drought on crop yield was quantified from the ISI-MP (Inter-Sectoral Impact Model Inter-comparison Project) crop model by a proposed non-linear ensemble of Random Forest (RF) and Gradient Boosting Machine (GBM). Results suggested that high drought magnitude with a longer drought duration is projected in some regions of South Asia while high drought intensity comes with a shorter duration. It was also found that Afghanistan, Pakistan, and India will experience a longer drought duration in the future. Our proposed ensemble machine learning (EML) approach had high predictive skill with a minimum value of RMSE (0.358-0.390), MAE (0.222-0.299), and a maximum value of R² (0.705-0.918) compared to the stand-alone methods of RF and GBM for yield loss risk projection. The drought-driven impact on crop yield demonstrates a high risk of yield loss under extreme drought events, which will encounter 54.15%, 29.30%, and 50.66% loss in the future for rice, wheat, and maize crops, respectively. Furthermore, drought and yield loss risk dynamics suggested a one unit decrease in SPEI value would lead to a 14.2%, 7.5%, and 10.9% decrease in yield for rice, wheat, and maize crops, respectively. This study will provide a notable direction for policy agencies to build resistance to crop production against the drought impact in the regions that are critical to climate change.

Histone acetylation of 45S rDNA correlates with disrupted nucleolar organization during heat stress response in Zea mays L

The role of the nucleolus in plant response to heat stress remains largely obscure. Our current efforts focused on exploring the underlying mechanism by which nucleolar disorganization is regulated in heat stressed-maize lines. Here, two maize lines, a heat-sensitive line, ZD958, and a heat-tolerant line, ZDH, were submitted to heat stress for investigating their association with the nucleolar disruption. Immunofluorescence staining showed that nucleolar disruption increased with prolonged treatment time. After heat treatment, a significant change in nucleolus organization was observed in the ZD958 line, but the ZDH line showed mild alteration. Moreover, actinomycin D (ActD)-induced nucleolus fission led to inhibition of maize growth under the normal condition. The ZD958 line exhibited a significant increase in the level of H3K9ac and H4K5ac of the 45S rDNA accompanied by a higher transcription of the 5'-external transcribed spacer (ETS) region, while the line ZDH showed a slight increase in histone acetylation levels and the transcriptional initiation at this site after heat treatment. To our knowledge, this is the first report providing a comparative insight between heat stress, rDNA histone modifications, and nucleolus disintegration in a heat-tolerant ZDH compared with a heat-sensitive line ZD958. Our investigation might assist maize breeders in obtaining heat-tolerant lines by targeting nucleoli using epigenetics.

Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data

Timely and reliable estimations of crop yield are essential for crop management and successful food trade. In previous studies, remote sensing data or climate data are often used alone in statistical yield estimation models. In this study, we synthetically used agrometeorological indicators and remote sensing vegetation parameters to estimate maize yield in Jilin and Liaoning Provinces of China. We applied two methods to select input variables, used the random forest method to establish yield estimation models, and verified the accuracy of the models in three disaster years (1997, 2000, and 2001). The results show that the R2 values of the eight yield estimation models established in the two provinces were all above 0.7, Lin’s concordance correlation coefficients were all above 0.84, and the mean absolute relative errors were all below 0.14. The mean absolute relative error of the yield estimations in the three disaster years was 0.12 in Jilin Province and 0.13 in Liaoning Province. A model built using variables selected by a two-stage importance evaluation method can obtain a better accuracy with fewer variables. The final yield estimation model of Jilin province adopts eight independent variables, and the final yield estimation model of Liaoning Province adopts nine independent variables. Among the 11 adopted variables in two provinces, ATT (accumulated temperature above 10 °C) variables accounted for the highest proportion (54.54%). In addition, the GPP (gross primary production) anomaly in August, NDVI (Normalized Difference Vegetation Index) anomaly in August, and standardized precipitation index with a two-month scale in July were selected as important modeling variables by all methods in the two provinces. This study provides a reference method for the selection of modeling variables, and the results are helpful for understanding the impact of climate on potential yield.

Identification and characterization of heat-responsive microRNAs at the booting stage in two rice varieties, 9311 and Nagina 22

MicroRNAs (miRNAs) are small, non-coding, regulatory RNAs that play important roles in abiotic stress responses in plants, but their regulatory roles in the adaptive response to heat stress at the booting stage in two rice varieties, 9311 and Nagina 22, remain largely unknown. In this study, 464 known miRNAs and 123 potential novel miRNAs were identified. Of these miRNAs, a total of 90 differentially expressed miRNAs were obtained with 9311 libraries as the control group, of which 54 were upregulated and 36 were downregulated. To gain insight into functional significance, 2773 potential target genes of these 90 differentially expressed miRNAs were predicted. GO enrichment analysis showed that the predicted target genes of differentially expressed miRNAs included NACs, LACs, CSD, and Hsp40. KEGG pathway analysis showed that the target genes of these differentially expressed miRNAs were significantly enriched in the plant hormone signal transduction pathway. The expression levels of 10 differentially expressed miRNAs and their target genes obtained by qRT-PCR were largely consistent with the sequencing results. This study lays a foundation for the elucidation of the miRNA-mediated regulatory mechanisms in rice at elevated temperatures.

Engineering 6-phosphogluconate dehydrogenase improves grain yield in heat-stressed maize

Endosperm starch synthesis is a primary determinant of grain yield and is sensitive to high-temperature stress. The maize chloroplast-localized 6-phosphogluconate dehydrogenase (6PGDH), PGD3, is critical for endosperm starch accumulation. Maize also has two cytosolic isozymes, PGD1 and PGD2, that are not required for kernel development. We found that cytosolic PGD1 and PGD2 isozymes have heat-stable activity, while amyloplast-localized PGD3 activity is labile under heat stress conditions. We targeted heat-stable 6PGDH to endosperm amyloplasts by fusing the Waxy1 chloroplast targeting the peptide coding sequence to the Pgd1 and Pgd2 open reading frames (ORFs). These WPGD1 and WPGD2 fusion proteins import into isolated chloroplasts, demonstrating a functional targeting sequence. Transgenic maize plants expressing WPGD1 and WPGD2 with an endosperm-specific promoter increased 6PGDH activity with enhanced heat stability in vitro. WPGD1 and WPGD2 transgenes complement the pgd3-defective kernel phenotype, indicating the fusion proteins are targeted to the amyloplast. In the field, the WPGD1 and WPGD2 transgenes can mitigate grain yield losses in high-nighttime-temperature conditions by increasing kernel number. These results provide insight into the subcellular distribution of metabolic activities in the endosperm and suggest the amyloplast pentose phosphate pathway is a heat-sensitive step in maize kernel metabolism that contributes to yield loss during heat stress.

Engineering 6-phosphogluconate dehydrogenase improves grain yield in heat-stressed maize

Endosperm starch synthesis is a primary determinant of grain yield and is sensitive to high-temperature stress. The maize chloroplast-localized 6-phosphogluconate dehydrogenase (6PGDH), PGD3, is critical for endosperm starch accumulation. Maize also has two cytosolic isozymes, PGD1 and PGD2, that are not required for kernel development. We found that cytosolic PGD1 and PGD2 isozymes have heat-stable activity, while amyloplast-localized PGD3 activity is labile under heat stress conditions. We targeted heat-stable 6PGDH to endosperm amyloplasts by fusing the Waxy1 chloroplast targeting the peptide coding sequence to the Pgd1 and Pgd2 open reading frames (ORFs). These WPGD1 and WPGD2 fusion proteins import into isolated chloroplasts, demonstrating a functional targeting sequence. Transgenic maize plants expressing WPGD1 and WPGD2 with an endosperm-specific promoter increased 6PGDH activity with enhanced heat stability in vitro. WPGD1 and WPGD2 transgenes complement the pgd3-defective kernel phenotype, indicating the fusion proteins are targeted to the amyloplast. In the field, the WPGD1 and WPGD2 transgenes can mitigate grain yield losses in high-nighttime-temperature conditions by increasing kernel number. These results provide insight into the subcellular distribution of metabolic activities in the endosperm and suggest the amyloplast pentose phosphate pathway is a heat-sensitive step in maize kernel metabolism that contributes to yield loss during heat stress.

Changes in the drought sensitivity of US maize yields

As climate change leads to increased frequency and severity of drought in many agricultural regions, a prominent adaptation goal is to reduce the drought sensitivity of crop yields. Yet many of the sources of average yield gains are more effective in good weather, leading to heightened drought sensitivity. Here we consider two empirical strategies for detecting changes in drought sensitivity and apply them to maize in the United States, a crop that has experienced myriad management changes including recent adoption of drought-tolerant varieties. We show that a strategy that utilizes weather-driven temporal variations in drought exposure is inconclusive because of the infrequent occurrence of substantial drought. In contrast, a strategy that exploits within-county spatial variability in drought exposure, driven primarily by differences in soil water storage capacity, reveals robust trends over time. Yield sensitivity to soil water storage increased by 55% on average across the US Corn Belt since 1999, with larger increases in drier states. Although yields have been increasing under all conditions, the cost of drought relative to good weather has also risen. These results highlight the difficulty of simultaneously raising average yields and lowering drought sensitivity.

The shifting influence of future water and temperature stress on the optimal flowering period for wheat in Western Australia

An optimal flowering period (OFP) minimises the long-term combined risk of extreme weather events on crop yield and exists in all environments. With climate change, the frequency, timing and intensity of these events are likely to change, which in turn may shift the OFP. It is important to explore how the OFP would change under a future climate. Knowledge of the OFP is important for formulating breeding strategies and developing suitable varieties. Here, a simulation analysis was conducted at 4 sites in Western Australia to quantify any shift in the OFP due to climate change, by accounting for the effects of frost, heat and water stress on wheat yield. Three global climate models that projected the greatest precipitation decrease under the Representative Concentration Pathways 8.5 during 2061-2100 were ensembled to represent a dry future climate condition (dry scenario); and 3 models that predicted the smallest decrease in precipitation were ensembled to represent a wet future climate condition (wet scenario). The simulation results predicted that the timing of OFPs for wheat in Western Australia would occur earlier than the current OFP. On average the OFP was 29 days earlier in the dry scenario and 11 days earlier in the wet scenario. Early sowing of long-season varieties would be preferable to achieve the OFP in both climate scenarios due to greater yield potential. Early sowing opportunities were very limited under the dry scenario, and therefore fast maturing varieties for late sowing would also be necessary.

Increased future occurrences of the exceptional 2018-2019 Central European drought under global warming

Since the spring 2018, a large part of Europe has been in the midst of a record-setting drought. Using long-term observations, we demonstrate that the occurrence of the 2018-2019 (consecutive) summer drought is unprecedented in the last 250 years, and its combined impact on the growing season vegetation activities is stronger compared to the 2003 European drought. Using a suite of climate model simulation outputs, we underpin the role of anthropogenic warming on exacerbating the future risk of such a consecutive drought event. Under the highest Representative Concentration Pathway, (RCP 8.5), we notice a seven-fold increase in the occurrence of the consecutive droughts, with additional 40 ([Formula: see text]) million ha of cultivated areas being affected by such droughts, during the second half of the twenty-first century. The occurrence is significantly reduced under low and medium scenarios (RCP 2.6 and RCP 4.5), suggesting that an effective mitigation strategy could aid in reducing the risk of future consecutive droughts.

Regional Climatological Drought: An Assessment Using High-Resolution Data

Regional assessments of droughts are limited, and meticulous assessments over larger spatial scales are generally not substantial. Understanding drought variability on a regional scale is crucial for enhancing the resiliency and adaptive ability of water supply and distribution systems. Moreover, it can be essential for appraising the dynamics and projection of droughts based on regional climate across various spatial and temporal scales. This work focuses on drought analysis using a high-resolution dataset for three drought-prone regions of India between 1950 and 2016. This study also uses monthly values of the self-calibrating Palmer Drought Severity Index (scPDSI), incorporating Penman–Monteith approximation, which is physically based on potential evapotranspiration. Climate data are statistically downscaled and formulated to form a timeline for characterizing major drought events. The downscaled climate data hold a good statistical agreement with station data with correlation coefficients (R) ranging from 0.91 to 0.96. Drought analysis indicates and identifies several major incidences over the analysis time period considered in this work, which truly adheres to the droughts recorded in reports of various literatures for those regions.

Spatiotemporal impacts of climate change on food production: case study of Shaanxi Province, China

The climate change on the impact of grain production potential has significant regional differences. Researchers have studied the grain production potential of various crop combinations or focused on single crop types in a typical area; however, the regional differences of the climate change on the impact of grain production potential were neglected. This paper used the Global Agro-Ecological Zone (GAEZ 3.0) model to focus on the analysis what is the climate change on the impact of grain production potential in different geographic units (Northern Shaanxi Plateau, Guanzhong Basin, Qinba Mountain) in Shaanxi Province of China. The case showed that the precipitation (Pre) what made changes of grain production potential was the most important factor in different geographic units. The increase of Pre had a positive impact on the grain production potential in Northern Shaanxi Plateau and Guanzhong Basin. However, in Qinba Mountain, due to excessive Pre in the Qinba Mountains, the decrease of Pre had a certain positive impact on the grain production potential. The precipitation was less in the Northern Shaanxi Plateau; therefore, its major factors leading to changes of crop production were precipitation and rainfall days. The increase of the mean maximum temperature (Tmx) and the mean minimum temperature (Tmn) had a positive impact of the grain production potential in the Northern Shaanxi Plateau and Guanzhong Basin. The higher temperature had a negative impact on the grain production potential. In Qinba Mountain, the increase of the temperature has a certain negative impact on the grain production potential. It has more influence of Tmx in the Guanzhong Basin and Qinba Mountain rather than that in the Northern Shaanxi Plateau. Generally speaking, the major climatic factors leading grain production potential were Pre and Tmx in Guanzhong Basin and Qinba Mountain.

Quantifying likelihoods of extreme occurrences causing maize yield reduction at the global scale

A variety of weather and climate extremes (e.g., droughts, heatwaves) can lead to negative impacts on crop yields and food security. It is thus important to understand likelihoods of extreme occurrences causing crop yield reduction for enhanced resilience of the food system. Here, we investigate the likelihood of occurrences of dry, hot, and compound dry-hot conditions causing crop yield reduction for ten maize-producing countries based on climate observations and country-level maize yields for the period 1961-2016. The likelihood of occurrences of different extremes causing maize yield reduction is quantified using a multivariate statistical model. Results show that the multivariate model performs well in quantifying the likelihood of extreme occurrences (i.e., dry, hot and compound dry-hot conditions) causing maize yield reduction. Overall, the likelihood of occurrences of the above three conditions leading to yield reduction varies among ten maize-producing countries and that of compound dry-hot condition is the highest for most countries, which is shown to be closely related to the precipitation-temperature dependence of each country. Moreover, the likelihood of compound dry-hot occurrences becomes higher as the severity of crop yield reduction increases. These findings highlight significant impacts of compound dry-hot conditions on maize yield reduction and provide valuable information for formulating effective agricultural measures under global warming.

High-resolution and bias-corrected CMIP5 projections for climate change impact assessments

Projections of climate change are available at coarse scales (70–400 km). But agricultural and species models typically require finer scale climate data to model climate change impacts. Here, we present a global database of future climates developed by applying the delta method –a method for climate model bias correction. We performed a technical evaluation of the bias-correction method using a ‘perfect sibling’ framework and show that it reduces climate model bias by 50–70%. The data include monthly maximum and minimum temperatures and monthly total precipitation, and a set of bioclimatic indices, and can be used for assessing impacts of climate change on agriculture and biodiversity. The data are publicly available in the World Data Center for Climate (WDCC; cera-www.dkrz.de), as well as in the CCAFS-Climate data portal (http://ccafs-climate.org). The database has been used up to date in more than 350 studies of ecosystem and agricultural impact assessment.

Measurement(s)	climate change • precipitation process • precipitation amount • consecutive dry months index per time period • temperature of air
Technology Type(s)	computational modeling technique
Factor Type(s)	spatial region
Sample Characteristic - Environment	climate system
Sample Characteristic - Location	Earth (planet)

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11353664

Leveraging drought risk reduction for sustainable food, soil and climate via soil organic carbon sequestration

Drought is a major risk in global agriculture. Building-up soil organic carbon (SOC) enhances soil fertility and efficient use of rainwater, which can increase drought tolerance in food production. SOC management demonstrates its benefit at various locations and is a promising means to achieve food security and climate mitigation at once. However, no global assessment of its potential and co-benefits gained from SOC enhancement has been presented. Here we evaluated the extent to which SOC build-up could reduce agricultural drought risk. Using statistical analysis of spatially-explicit global crop and soil datasets, we find that relatively small enhancement in topsoil (0–30 cm) organic carbon content (OC_top) could increase drought tolerance of the food production systems operating over 70% of the global harvested area (particularly drylands). By closing the gap between current and upper limit of tolerance levels through SOC addition of 4.87 GtC at the global scale, farmer’s economic output in drought years would increase by ~16%. This level of SOC increase has co-benefit of reducing global decadal mean temperature warming by 0.011 °C. Our findings highlight that progress towards multiple development goals can be leveraged by SOC enhancement in carbon (C)-poor soils in drier regions around the world.

Probabilistic evaluation of the impact of compound dry-hot events on global maize yields

Weather and climate extremes, such as droughts and hot extremes, may result in marked damages to crop yields and threaten regional and global food security. Understanding the relationship between climate extremes and crop yields is of critical importance for food security under a changing climate. The objective of this study is to investigate the probabilistic variability of maize yields with respect to compound dry-hot events, which has been shown to be more stressful to crops compared with individual dry or hot events. A multivariate model is first constructed to model the joint behavior of the dry condition, hot condition, and crop yields. The response of crop yields under different dry, hot, and compound dry-hot conditions at national and global scales is then investigated based on the conditional distribution. For the major maize producing countries (top 5), the probability of maize yield reduction could increase by from 0.07 to 0.31 (from 0.04 to 0.31) when the individual extreme drought (extreme hot) conditions changed to compound dry-hot conditions. The probabilistic evaluation of compound dry-hot events' impacts on maize yields is expected to provide useful insights for the mitigation of compound events and their impacts under a changing climate.

Effect of Climate Change on Maize Yield in the Growing Season: A Case Study of the Songliao Plain Maize Belt

Based on the 1965–2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998–2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized precipitation evapotranspiration index (SPEI) and the Mann–Kendall mutation test; furthermore, the relationship between meteorological factors, drought index, and maize climate yield was determined. Finally, the maize climate yields under 1.5 °C and 2.0 °C global warming scenarios were predicted. The results revealed that: (1) from 1965 to 2017, the study area experienced increasing temperature, decreasing precipitation, and intensifying drought trends; (2) the yield of the study area showed a downward trend from 1998 to 2017. Furthermore, the climate yield was negatively correlated with temperature, positively correlated with precipitation, and positively correlated with SPEI-1 and SPEI-3; and (3) under the 1.5 °C and the 2.0 °C global warming scenarios, the temperature and the precipitation increased in the maize growing season. Furthermore, under the studied global warming scenarios, the yield changes predicted by multiple regression were −7.7% and −15.9%, respectively, and the yield changes predicted by one-variable regression were −12.2% and −21.8%, respectively.

Effects of long-term exposure to elevated temperature on Zea mays endosperm development during grain fill

Cereal yields decrease when grain fill proceeds under conditions of prolonged, moderately elevated temperatures. Endosperm-endogenous processes alter both rate and duration of dry weight gain, but underlying mechanisms remain unclear. Heat effects could be mediated by either abnormal, premature cessation of storage compound deposition or accelerated implementation of normal development. This study used controlled environments to isolate temperature as the sole environmental variable during Zea mays kernel-fill, from 12 days after pollination to maturity. Plants subjected to elevated day, elevated night temperatures (38°C day, 28°C night (38/28°C])) or elevated day, normal night (38/17°C), were compared with those from controls grown under normal day and night conditions (28/17°C). Progression of change over time in endosperm tissue was followed to dissect contributions at multiple levels, including transcriptome, metabolome, enzyme activities, product accumulation, and tissue ultrastructure. Integrated analyses indicated that the normal developmental program of endosperm is fully executed under prolonged high-temperature conditions, but at a faster rate. Accelerated development was observed when both day and night temperatures were elevated, but not when daytime temperature alone was increased. Although transcripts for most components of glycolysis and respiration were either upregulated or minimally affected, elevated temperatures decreased abundance of mRNAs related to biosynthesis of starch and storage proteins. Further analysis of 20 central-metabolic enzymes revealed six activities that were reduced under high-temperature conditions, indicating candidate roles in the observed reduction of grain dry weight. Nonetheless, a striking overall resilience of grain filling in the face of elevated temperatures can be attributed to acceleration of normal endosperm development.

The miRNA-Mediated Post-Transcriptional Regulation of Maize in Response to High Temperature

High temperature (HT) has recently become one of the most important abiotic stresses restricting crop production worldwide. MicroRNAs (miRNAs) are important regulators in plant development and stress responses. However, knowledge of miRNAs of maize in response to HT is limited. In this study, we simultaneously adopted miRNA sequencing and transcriptome profiling to analyze the differential expression of miRNAs and mRNAs in maize during exposure to HT stress. Our analysis revealed 61 known miRNAs belonging to 26 miRNA families and 42 novel miRNAs showing significant differential expression, with the majority being downregulated. Meanwhile, the expression of 5450 mRNAs was significantly altered in the same stressed tissues. Differentially expressed transcripts were most significantly associated with response to stress, photosynthesis, biosynthesis of secondary metabolites, and signal transduction pathways. In addition, we discovered 129 miRNA–mRNA pairs that were regulated antagonistically, and further depiction of the targeted mRNAs indicated that several transcription factors, protein kinases, and receptor-like-protein-related transmembrane transport and signaling transduction were profoundly affected. This study has identified potential key regulators of HT-stress response in maize and the subset of genes that are likely to be post-transcriptionally regulated by miRNAs under HT stress.

Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015

19,980 crop yield forecasts have been published for the European Union (EU) Member States (MS) during 1993-2015 using the MARS-Crop Yield Forecasting System (MCYFS). We assess the performance of these forecasts for soft wheat, durum wheat, grain maize, rapeseed, sunflower, potato and sugar beet, and sought to answer three questions. First, how good has the system performed? This was investigated by calculating several accuracy indicators (e.g. the mean absolute percentage error, MAPE) for the first forecasts during a season, forecasts one month pre-harvest, and the end-of-campaign (EOC) forecasts during 2006-2015 using reported yields. Second, do forecasts improve during the season? This was evaluated by comparing the accuracy of the first, the pre-harvest, and the EOC forecasts. Third, have forecasts systematically improved year-to-year? This was quantified by testing whether linear models fitted to the median of the national level absolute relative forecast errors for each crop at EU-12 (EU-27) level from 1993 to 2015 (2006-2015) were characterized by significant negative slopes. Encouragingly, the lowest median MAPE across all crops is obtained for Europe's largest producer, France, equalling 3.73%. Similarly, the highest median MAPE is obtained for Portugal, at 14.37%. Forecasts generally underestimated reported yields, with a systematic underestimation across all MS for soft wheat, rapeseed and sugar beet forecasts. Forecasts generally improve during the growing season; both the forecast error and its variability tend to progressively decrease. This is the case for the cereals, and to a lesser extent for the tuber crops, while seasonal forecast improvements are lower for the oilseed crops. The median EU-12 yield forecasts for rapeseed, potato and sugar beet have significantly (p-value < 0.05) improved from 1993 to 2015. No evidence was found for improvements for the other crops, neither was there any significant improvement in any of the crop forecasts from 2006 to 2015, evaluated at EU-27 level. In the early years of the MCYFS, most of the yield time series were characterized by strong trends; nowadays yield growth has slowed or even plateaued in several MS. In addition, an increased volatility in yield statistics is observed, and while crop yield forecasts tend to improve in a given year, in recent years, there is no evidence of structural improvements that carry-over from year-to-year. This underlines that renewed efforts to improve operational crop yield forecasting are needed, especially in the light of the increasingly variable and occasionally unprecedented climatic conditions impacting the EU's crop production systems.

From extreme weather to impacts: The role of the areas of concern maps in the JRC MARS bulletin

Each month the JRC issues the MARS Bulletin detailing the agro-meteorological and expert analysis underpinning the assessment of European crops' status and yield forecasts. In this context a resume is provided to give an overview on the geographical distribution of eventual crop damages. The MARS Bulletin provides such information in a set of synthetic maps (Areas of Concern), produced in each Bulletin, depicting extreme weather events and their impact on crops that have occurred in Europe during the analysis period. The present article describes the mix of quantitative and qualitative datasets and methodologies that drive the delineation of the Areas of Concern (AOC) maps and evaluates their capability to resemble crop production losses. The quantitative analysis is based on the Mars Crop Yield Forecasting System (MCYFS) indicators coming from meteorological models, crop growth models and remote sensing data. Indicators are considered in absolute and relative terms and in their relation with standard statistical metrics. The outcome of the quantitative analysis is a set of potential Areas of Concern. Experts' judgment is thus necessary to discriminate potential results through a qualitative analysis focused on: past occurred events and climatologic conditions; agro-management practices; regional agricultural systems peculiarity and their historical resilience and resistance to adverse conditions. In this article the experts' judgment of the team of current MARS analysts, as used in the AOC analysis, is translated into a warning index. Such index condenses the specific contribution to the final production of each development stage and the adverse agrometeorological events occurred, as depicted into the AOC maps. The warning index is computed at country scale for the past five agricultural seasons, from season 2011-2012 to season 2015-2016. Two crops are considered, winter wheat and grain maize as proxy for winter and summer crop groups. The warning indexes calculated are then compared to the national production in a qualitative way. To support the analysis few study cases are presented. The findings of this article highlight that the events depicted in the AOC maps are informative about production losses and specific knowledge is needed to full understand the information carried.

Keeping global warming within 1.5 °C reduces future risk of yield loss in the United States: A probabilistic modeling approach

This study assess the possible outcomes of yield changes in the United States which is responsible for 40% of global maize supply under 1.5 °C and 2 °C global warming scenarios. Instead of providing deterministic estimates, this study introduces a probability-based approach that allow for examination of the associated probability of each outcome, which has great implications for decision-makings. Results show distinct spatial patterns in future yield loss risk associated with temperature rise at the county scale, with highest probability in central and southeastern US, and lowest risk in western US and high production regions such as Iowa. Comparing the estimates under 1.5 °C global warming against that in 2.0 °C warming indicates that keeping global warming within 1.5 °C has great benefits for reducing future yield loss risk. Based on the ensemble mean of 97 climate model simulations, the risk of yield dropping below historical long-term mean is projected to decrease from 81% to 75% for the country as a whole. Such benefit is more evident when considering the risk of yield reduction by 10% and 20%, which is expected to decrease by 25% and 28%, respectively. This suggests that constraining global temperature rise to 1.5 °C has more benefits for reducing extreme yield reductions. Spatially, keeping global warming within 1.5 °C would benefit more in Missouri, South Dakota, Eastern Kansas, Southern Texas and southeastern part of the country than other regions, highlighting the spatially variable benefits of climate mitigation efforts. The analysis framework introduced in this study can also be easily extended to other regions and crops. The results of this study highlight the areas where maize yield is most vulnerable to temperature rise, and the spatially variable benefits for reducing yield loss risk by keeping global warming within 1.5 °C.

Yield trends, variability and stagnation analysis of major crops in France over more than a century

France is a major crop producer, with a production share of approx. 20% within the European Union. Yet, a discussion has recently started whether French yields are stagnating. While for wheat previous results are unanimously pointing to recent stagnation, there is contradictory evidence for maize and few to no results for other crops. Here we analyse a data set with more than 120,000 yield observations from 1900 to 2016 for ten crops (barley, durum and soft wheat, maize, oats, potatoes, rapeseed, sugar beet, sunflower and wine) in the 96 mainland French départements (NUTS3 administrative division). We dissect the evolution of yield trends over time and space, analyse yield variation and evaluate whether growth of yields has stalled in recent years. Yields have, on average across crops, multiplied four-fold over the course of the 20^th century. While absolute yield variability has increased, the variation relative to the mean has halved – mean yields have increased faster than their variability. But growth of yields has stagnated since the 1990’s for winter wheat, barley, oats, durum wheat, sunflower and wine on at least 25% of their areas. Reaching yield potentials is unlikely as a cause for stagnation. Maize, in contrast, shows no evidence for stagnation.

Can N management affect the magnitude of yield loss due to heat waves in wheat and maize?

Deleterious effects of heat on crop yields are well documented and the occurrence of heat stresses will likely be a major constraint to achieving increased yields of major crops. Thus, agronomic and genetic strategies for increased resilience to high temperatures will be necessary. Much of the work done on this area has been focused to identify genetic sources of increased resilience and much less has been done on the crop ecology side. Nitrogen (N) fertilization is within the most common management practices used in cereal production, however, there have been limited efforts to elucidate to what degree the level of soil fertility may affect the magnitude of the high temperature effect on crop yield. The likely interaction may be relevant for designing more appropriate fertilization strategies. We conducted different studies on maize (2009-2012) and wheat (2012-2013), always under field conditions, to determine whether the availability of N may be responsible for the magnitude of the yield penalty imposed by heat stress during reproductive phases (i.e. when heat waves are more likely). We concluded that sensitivity to heat stress increased with increasing N availability and speculated that moderate N stress might produce in the crop plants a sort of acclimation to reduce sensitivity to other stresses. Fertilisation recommendations in the future may need to balance the yielding benefits of high N availability with the detrimental effect of increasing sensitivity to heat stress.

GAMLSS: A distributional regression approach

Abstract:

A tutorial of the generalized additive models for location, scale and shape (GAMLSS) is given here using two examples. GAMLSS is a general framework for performing regression analysis where not only the location (e.g., the mean) of the distribution but also the scale and shape of the distribution can be modelled by explanatory variables.

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast

Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

Recent changes in county-level corn yield variability in the United States from observations and crop models

The United States is responsible for 35% and 60% of global corn supply and exports. Enhanced supply stability through a reduction in the year-to-year variability of US corn yield would greatly benefit global food security. Important in this regard is to understand how corn yield variability has evolved geographically in the history and how it relates to climatic and non-climatic factors. Results showed that year-to-year variation of US corn yield has decreased significantly during 1980-2010, mainly in Midwest Corn Belt, Nebraska and western arid regions. Despite the country-scale decreasing variability, corn yield variability exhibited an increasing trend in South Dakota, Texas and Southeast growing regions, indicating the importance of considering spatial scales in estimating yield variability. The observed pattern is partly reproduced by process-based crop models, simulating larger areas experiencing increasing variability and underestimating the magnitude of decreasing variability. And 3 out of 11 models even produced a differing sign of change from observations. Hence, statistical model which produces closer agreement with observations is used to explore the contribution of climatic and non-climatic factors to the changes in yield variability. It is found that climate variability dominate the change trends of corn yield variability in the Midwest Corn Belt, while the ability of climate variability in controlling yield variability is low in southeastern and western arid regions. Irrigation has largely reduced the corn yield variability in regions (e.g. Nebraska) where separate estimates of irrigated and rain-fed corn yield exist, demonstrating the importance of non-climatic factors in governing the changes in corn yield variability. The results highlight the distinct spatial patterns of corn yield variability change as well as its influencing factors at the county scale. I also caution the use of process-based crop models, which have substantially underestimated the change trend of corn yield variability, in projecting its future changes.

Linking crop yield anomalies to large-scale atmospheric circulation in Europe

Understanding the effects of climate variability and extremes on crop growth and development represents a necessary step to assess the resilience of agricultural systems to changing climate conditions. This study investigates the links between the large-scale atmospheric circulation and crop yields in Europe, providing the basis to develop seasonal crop yield forecasting and thus enabling a more effective and dynamic adaptation to climate variability and change. Four dominant modes of large-scale atmospheric variability have been used: North Atlantic Oscillation, Eastern Atlantic, Scandinavian and Eastern Atlantic-Western Russia patterns. Large-scale atmospheric circulation explains on average 43% of inter-annual winter wheat yield variability, ranging between 20% and 70% across countries. As for grain maize, the average explained variability is 38%, ranging between 20% and 58%. Spatially, the skill of the developed statistical models strongly depends on the large-scale atmospheric variability impact on weather at the regional level, especially during the most sensitive growth stages of flowering and grain filling. Our results also suggest that preceding atmospheric conditions might provide an important source of predictability especially for maize yields in south-eastern Europe. Since the seasonal predictability of large-scale atmospheric patterns is generally higher than the one of surface weather variables (e.g. precipitation) in Europe, seasonal crop yield prediction could benefit from the integration of derived statistical models exploiting the dynamical seasonal forecast of large-scale atmospheric circulation.

Interactions between temperature and drought in global and regional crop yield variability during 1961-2014

Inter-annual crop yield variation is driven in large parts by climate variability, wherein the climate components of temperature and precipitation often play the biggest role. Nonlinear effects of temperature on yield as well as interactions among the climate variables have to be considered. Links between climate and crop yield variability have been previously studied, both globally and at regional scales, but typically with additive models with no interactions, or when interactions were included, with implications not fully explained. In this study yearly country level yields of maize, rice, soybeans, and wheat of the top producing countries were combined with growing season temperature and SPEI (standardized precipitation evapotranspiration index) to determine interaction and intensification effects of climate variability on crop yield variability during 1961-2014. For maize, soybeans, and wheat, heat and dryness significantly reduced yields globally, while global effects for rice were not significant. But because of interactions, heat was more damaging in dry than in normal conditions for maize and wheat, and temperature effects were not significant in wet conditions for maize, soybeans, and wheat. Country yield responses to climate variability naturally differed between the top producing countries, but an accurate description of interaction effects at the country scale required sub-national data (shown only for the USA). Climate intensification, that is consecutive dry or warm years, reduced yields additionally in some cases, however, this might be linked to spillover effects of multiple growing seasons. Consequently, the effect of temperature on yields might be underestimated in dry conditions: While there were no significant global effects of temperature for maize and soybeans yields for average SPEI, the combined effects of high temperatures and drought significantly decreased yields of maize, soybeans, and wheat by 11.6, 12.4, and 9.2%, respectively.