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

Weather Extremes Shock Maize Production: Current Approaches and Future Research Directions in Africa

Extreme weather events have led to widespread yield losses and significant global economic damage in recent decades. African agriculture is particularly vulnerable due to its harsh environments and limited adaptation capacity. This systematic review analyzes 96 articles from Web of Science, Science Direct, and Google Scholar, focusing on biophysical studies related to maize in Africa and worldwide. We investigated the observed and projected extreme weather events in Africa, their impacts on maize production, and the approaches used to assess these effects. Our analysis reveals that drought, heatwaves, and floods are major threats to African maize production, impacting yields, suitable cultivation areas, and farmers' livelihoods. While studies have employed various methods, including field experiments, statistical models, and process-based modeling, African research is often limited by data gaps and technological constraints. We identify three main gaps: (i) lack of reliable long-term experimental and empirical data, (ii) limited access to advanced climate change adaptation technologies, and (iii) insufficient knowledge about specific extreme weather patterns and their interactions with management regimes. This review highlights the urgent need for targeted research in Africa to improve understanding of extreme weather impacts and formulate effective adaptation strategies. We advocate for focused research on data collection, technology transfer, and integration of local knowledge with new technologies to bolster maize resilience and food security in Africa.

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.

The Overexpression of Zea mays Strigolactone Receptor Gene D14 Enhances Drought Resistance in Arabidopsis thaliana L

Strigolactones (SLs) represent a recently identified class of plant hormones that are crucial for plant tillering and mycorrhizal symbiosis. The D14 gene, an essential receptor within the SLs signaling pathway, has been well-examined in crops, like rice (Oryza sativa L.) and Arabidopsis (Arabidopsis thaliana L.), yet the research on its influence in maize (Zea mays L.) remains scarce. This study successfully clones and establishes Arabidopsis D14 gene overexpression lines (OE lines). When compared with the wild type (WT), the OE lines exhibited significantly longer primary roots during germination. By seven weeks of age, these lines showed reductions in plant height and tillering, alongside slight decreases in rosette and leaf sizes, coupled with early aging symptoms. Fluorescence-based quantitative assays indicated notable hormonal fluctuations in OE lines versus the WT, implying that D14 overexpression disrupts plant hormonal homeostasis. The OE lines, exposed to cold, drought, and sodium chloride stressors during germination, displayed an especially pronounced resistance to drought. The drought resistance of OE lines, as evident from dehydration-rehydration assays, outmatched that of the WT lines. Additionally, under drought conditions, the OE lines accumulated less reactive oxygen species (ROS) as revealed by the assessment of the related physiological and biochemical parameters. Upon confronting the pathogens Pseudomonas syringae pv. tomato DC3000 (Pst DC3000), post-infection, fluorescence quantitative investigations showed a significant boost in the salicylic acid (SA)-related gene expression in OE lines compared to their WT counterparts. Overall, our findings designate the SL receptor D14 as a key upregulator of drought tolerance and a regulator in the biotic stress response, thereby advancing our understanding of the maize SL signaling pathway by elucidating the function of the pivotal D14 gene.

Heat stress affects tassel development and reduces the kernel number of summer maize

Maize grain yield is drastically reduced by heat stress (HTS) during anthesis and early grain filling. However, the mechanism of HTS in reproductive organs and kernel numbers remains poorly understood. From 2018 to 2020, two maize varieties (ND372, heat tolerant; and XY335, heat sensitive) and two temperature regimens (HTS, heat stress; and CK, natural control) were evaluated, resulting in four treatments (372CK, 372HTS, 335CK, and 335HTS). HTS was applied from the nine-leaf stage (V9) to the anthesis stage. Various morphological traits and physiological activities of the tassels, anthers, and pollen from the two varieties were evaluated to determine their correlation with kernel count. The results showed that HTS reduced the number of florets, tassel volume, and tassel length, but increased the number of tassel branches. HTS accelerates tassel degradation and reduces pollen weight, quantity, and viability. Deformation and reduction in length and volume due to HTS were observed in both the Nongda 372 (ND372) and Xianyu 335 (XY335) varieties, with the average reductions being 22.9% and 35.2%, respectively. The morphology of the anthers changed more conspicuously in XY335 maize. The number of kernels per spike was reduced in the HTS group compared with the CK group, with the ND372 and XY335 varieties showing reductions of 47.3% and 59.3%, respectively. The main factors underlying the decrease in yield caused by HTS were reductions in pollen quantity and weight, tassel rachis, and branch length. HTS had a greater effect on the anther shape, pollen viability, and phenotype of XY335 than on those of ND372. HTS had a greater impact on anther morphology, pollen viability, and the phenotype of XY335 but had no influence on the appearance or dissemination of pollen from tassel.

Changes in per capita wheat production in China in the context of climate change and population growth

To address challenges associated with climate change, population growth and decline in international trade linked to the COVID-19 pandemic, determining whether national crop production can meet populations' requirements and contribute to socio-economic resilience is crucial. Three crop models and three global climate models were used in conjunction with predicted population changes. Compared with wheat production in 2000-2010, total production and per capita wheat production were significantly (P < 0.05) increase in 2020-2030, 2030-2040 and 2040-2050, respectively, under RCP4.5 and RCP8.5 due to climate change in China. However, when considering population and climate changes, the predicted per capita production values were 125.3 ± 0.3, 127.1 ± 2.3 and 128.8 ± 2.7 kg during the 2020-2030, 2030-2040, 2040-2050 periods under RCP4.5, or 126.2 ± 0.7, 128.7 ± 2.5, and 131.0 ± 4.1 kg, respectively, under RCP8.5. These values do not significantly differ (P > 0.05) from the baseline level (127.9 ± 1.3 kg). The average per capita production in Loess Plateau and Gansu-Xinjiang subregions declined. In contrast, per capita production in the Huanghuai, Southwestern China, and Middle-Lower Yangtze Valleys subregions increased. The results suggest that climate change will increase total wheat production in China, but population change will partly offset the benefits to the grain market. In addition, domestic grain trade will be influenced by both climate and population changes. Wheat supply capacity will decline in the main supply areas. Further research is required to address effects of the changes on more crops and in more countries to obtain deeper understanding of the implications of climate change and population growth for global food production and assist formulation of robust policies to enhance food security.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12571-023-01351-x.

Risk of Crop Yield Reduction in China under 1.5 °C and 2 °C Global Warming from CMIP6 Models

Warmer temperatures significantly influence crop yields, which are a critical determinant of food supply and human well-being. In this study, a probabilistic approach based on bivariate copula models was used to investigate the dependence (described by joint distribution) between crop yield and growing season temperature (T_GS) in the major producing provinces of China for three staple crops (i.e., rice, wheat, and maize). Based on the outputs of 12 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathway 5-8.5, the probability of yield reduction under 1.5 °C and 2 °C global warming was estimated, which has great implications for agricultural risk management. Results showed that yield response to T_GS varied with crop and region, with the most vulnerable being rice in Sichuan, wheat in Sichuan and Gansu, and maize in Shandong, Liaoning, Jilin, Nei Mongol, Shanxi, and Hebei. Among the selected five copulas, Archimedean/elliptical copulas were more suitable to describe the joint distribution between T_GS and yield in most rice-/maize-producing provinces. The probability of yield reduction was greater in vulnerable provinces than in non-vulnerable provinces, with maize facing a higher risk of warming-driven yield loss than rice and wheat. Compared to the 1.5 °C global warming, an additional 0.5 °C warming would increase the yield loss risk in vulnerable provinces by 2-17%, 1-16%, and 3-17% for rice, wheat, and maize, respectively. The copula-based model proved to be an effective tool to provide probabilistic estimates of yield reduction due to warming and can be applied to other crops and regions. The results of this study demonstrated the importance of keeping global warming within 1.5 °C to mitigate the yield loss risk and optimize agricultural decision-making in vulnerable regions.

Future global concurrent droughts and their effects on maize yield

Droughts are one of the most devastating natural disasters. Droughts can co-exist in different forms (e.g. meteorological, hydrological, and agricultural) as concurrent droughts. Such concurrent droughts can have far reaching implications for crop yield and global food security. The present study aims to assess global concurrent drought traits and their effects on maize yield under climate change. The standardized indices of precipitation, runoff, and soil moisture incorporated as multivariate standardized drought index (MSDI) using copula functions are used to quantify the concurrent droughts. The ensemble data of several General Circulation Models (GCMs) considering the high emission scenario of Coupled Model Intercomparison Project phase 6 (CMIP6) are utilized. Applying run theory on a time series (1950-2100) of MSDI values, the duration, severity, areal coverage, and average areal intensity of concurrent droughts are computed. The temporal evolution of drought duration and severity are compared among historical (1950-2014), near future (2021-2060), and far future (2061-2100) timeframes. The results indicate that the most vulnerable regions in the late 21st century are Central America, the Mediterranean, Southern Africa, and the Amazon basin. The indices and spatial extent of the individual droughts are used as predictor variables to predict the country-level crop index of the top seven producers of maize. The historical dynamics between maize yield and different drought forms are projected using XGBoost (Extreme Gradient Boosting) algorithms. The future temporal changes in drought-crop yield dynamics are tracked using probabilities of various drought forms under yield-loss conditions. The conditional concurrent drought probabilities are as high as 84 %, 64 %, and 37 % in France, Mexico, and Brazil, revealing that concurrent drought affects the maize yield tremendously in the far future. This approach of applying statistical and soft-computing techniques could aid in drought mitigation under changing climatic conditions.

Assessment and Prediction of Grain Production Considering Climate Change and Air Pollution in China

This study examines the spatial and temporal impacts of climate change on grain production in China. This is achieved by establishing a spatial error model consisting of four indicators: the climate, air pollution, economic behavior, and agricultural technology, covering 31 provinces in China from 2004 to 2020. These indicators are used to validate the spatial impacts of climate change on grain production. Air pollution data are used as instrumental variables to address the causality between climate and grain production. The regression results show that: First, climatic variables all have a non-linear “increasing then decreasing” effect on food production. Second, SO2, PM10, and PM2.5 have a negative impact on grain production. Based on the model, changes in the climatic production potential of grain crops can be calculated, and the future spatial layout of climate production can also be predicted by using random forests. Studies have shown that the median value of China’s grain production potential is decreasing, and the low value is increasing.

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.

Disentangling increasing compound extremes at regional scale during Indian summer monsoon

Compound extremes exhibit greater adverse impacts than their univariate counterparts. Studies have reported changes in frequency and the spatial extent of extremes in India; however, investigation of compound extremes is in the infancy state. This study investigates the historical variation of compound dry and hot extremes (CDHE) and compound wet and cold extremes (CWCE) during the Indian summer monsoon period from 1951 to 2019 using monthly data. Results are analyzed for 10 identified homogeneous regions for India. Our results unravelled that CDHE (CWCE) frequency has increased (decreased) by 1-3 events per decade for the recent period (1977-2019) relative to the base period (1951-1976). Overall, the increasing (decreasing) pattern of CDHE (CWCE) is high across North-central India, Western India, North-eastern India and South-eastern coastlines. Our findings help in identification of the parts of the country affected by frequent and widespread CDHE during the recent period, which is alarming. More detailed assessments are required to disentangle the complex physical process of compound extremes to improve risk management options.