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Extreme weather can have negative impacts on crop production. In this study, we statistically estimate the impacts of dry days, heat waves, and cold days on maize yield based on household survey data from 1993 to 2011 in ten villages of Shanxi province, China. Our results show that dry days, heat waves, and cold days have negative effects on maize...
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... study region is ten villages evenly located in Shanxi province of North China between latitude 34 • 34 -40 • 44 north and longitude 110 • 15 -114 • 32 east (Figure 1). In 2013, the rural population accounts for 47.4% of the total population of 36.3 million in Shanxi [14]. ...
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... Despite poor environmental issues, high daily temperature in the rainy season led to a higher accumulation of the growing degree days (GDD) and faster corn phenology such as flowering and maturing times in tropical and subtropical regions (Hou et al. 2014;Jiang et al. 2020;Sintanaparadee et al. 2022). Early flowering implied that the vegetative growth period was shorter; thus, it may reduce aboveground biomass, yield components, and grain yield (Liu et al. 2013;Shim et al. 2017;Wei et al. 2017;Lizaso et al. 2018;Sah et al. 2020). High temperature and relative humidity triggered greater severity of tropical diseases such as stalk and ear rots (Prasanna et al. 2021), becoming another major constraint for maize growth and development. ...
Maize hybrids become more acceptable than other cultivar types since they promise high yield and uniformity by exploiting the heterosis advantages. Numerous lines are to be crossed to obtain the promising hybrids, and the progenies must be tested in multi-environment trials. Handling large number of genotypes in routine work is resource intensive; thus, the question arises if hybrid prediction based on the mid-parent (MP) value is feasible to reduce the workload. This study aimed to investigate the reliability of MP values in hybrid prediction through simple linear correlation and to estimate the magnitude of heterosis on given traits in sweet-waxy corn. Six parental lines and 10 F1 hybrids were evaluated in Khon Kaen in the dry and rainy seasons (2021/2022). Genotype and the interaction between genotype and season were significant for all observed traits. Waxy corn genotype 8A3-B was a good combiner for yield and yield components. Heterosis was trait-dependent, and it could imply parental adaptation when the estimation was done in contrasting environments. We found that the MP value can be used to predict the hybrid performance for flowering times only, but this approach was not effective for the rest agronomic traits. Since commonly, maize improvements are regarding multiple favorable traits, the strategy to use MP as single factor in hybrid prediction was not reliable. Other approaches, either SCA alone or the sum of mid-parent GCA and SCA, could be implemented for hybrid prediction in future studies.
... There was a negative association between increasing temperature and maize yield [54,55] in which each additional GDD30 + led to a yield reduction of about 1.0-1.2% [29,56]. High temperatures contributed to yield reduction, not only because of the damage to maize flowers during daytime [30], but also because of increased respiration and decreased net dry matter accumulation during nighttime [57]. ...
Tropicalization is one of the major objectives in breeding haploid inducers to address the poor adaptation of temperate haploid inducers in doubled haploid production in tropical maize. Gaining a better understanding of weather profiles in targeted agroecology is important. This study aimed to investigate the seasonal variation of tropical savanna climate and its impact on agronomic traits and haploid induction rate (HIR) of Stock-6-derived haploid inducer lines. A total of 14 hap-loid inducers were evaluated across two typical growing seasons between 2020 and 2021. Weather data were collected on daily minimum and maximum temperatures, relative humidity, precipitation , and solar radiation whereas phenotypic data were recorded on plant phenology, tassel attributes , plant stature, ear components, inducer seed rate (ISR), and HIR. The effects of season, genotype , and genotype by season were significant for all traits except season factor on ISR. Seasonal variation existed where the dry season was more suitable for haploid induction and inducer maintenance , as haploid inducers revealed better agronomic performance and seed set, delayed flowering dates, and higher HIR. Since the crossover performance of haploid inducers over seasons was detected , further implications on genotype selection in each season are discussed.
... 3) The calculation of the heat stress index, HDD, could benefit from the cosine function approach (Schlenker and Roberts, 2009). We tested the cosine function approach by using daily minimum and maximum temperature (Wei et al., 2017), but found no significant impacts on our results ( Figure A3). As our precipitation data were still on daily basis, we have decided to keep the result of HDD and FDD both calculated from daily temperature data. ...
... We tested the use of cosine function method (Wei et al., 2017) to calculate hourly temperature based on daily minimum and maximum temperature, then to obtain the heating degree days (HDD). Hourly temperature (Th) is expressed by a cosinusoidal function assuming that the maximum temperature occurs at 14:00: ...
Understanding the changes in the frequency and intensity of compound agroclimatic extremes is important for studying the resilience of the food system under anthropogenic warming. However, the spatiotemporal variation of compound agroclimatic extremes for specific crops, and the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations in reproducing them, have largely been under-addressed. Herein, we have investigated the spatiotemporal variation of the occurrences and intensities of four compound agroclimatic extremes (hot-wet, hot-dry, cold-wet, cold-dry) for maize harvested areas in China during 1990–2014 and examined the capability of the CMIP6 to capture these variations. The results did not reveal any significant trends but there was a pronounced interannual volatility in both the occurrence and intensity of compound agroclimatic extremes. Seventy one percent of the maize harvested areas in China experienced at least one compound extreme event during the study period. Hotspots for high occurrence and high intensity included the major plains in the Huang-Huai-Hai and Southern maize regions. In general, five general circulation models (GCMs) from the CMIP6 relatively poorly captured the variation of the occurrence and intensity, and reported lower interannual volatility and less regional disparity compared to the observations, and showed weakness in capturing time-specific events. At the national scale, the five GCM ensemble mean outperformed any single model, and no single GCM outperformed any other individual GCMs. The best model differed according to the specific region of interest and the type of event. Our results highlight the need to improve field management decisions and adopt adaptation strategies in accord with local conditions to reduce the potential impacts on food systems.
... Vogel et al. (2019) applied machine learning algorithms (random forests) to analyse the impact of extreme weather conditions on yields of maize, soybeans, rice and spring wheat. Wei et al. (2017) calculated the effect of extreme climate including high temperature and drought on maize yield in Shanxi Province by linear regression. Wang et al., (2021) used spatial clustering and mixed linear models to evaluate the impact of climatic factors and drought on maize yields in eastern Northwest China. ...
The study of the impact of high temperature and drought on the yield of major staple crops can provide important scientific support for the decision-making of agricultural sustainable development. Based on the temperature and precipitation data of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA 5 for northern China, this paper calculates three indexes, the standard precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI) and the extreme degree-day (EDD), from 1979 to 2017. Monthly SPI and monthly SPEI were calculated at 1 – to – 12 month lags, and EDD was calculated per crop growth season. The yield of winter wheat, spring wheat and summer maize in each province of the study area from 1979 to 2017 was de-trended, and the relative fluctuation of the yield of the three crops was calculated. The change trends of SPI, SPEI and EDD were analysed using the Mann–Kendall test and Sen's slope. The single and interactive effects of high temperature and drought on crop yield were studied using multidimensional Copula function. The results show that: 1) Both high temperature and drought stress in northern China show an increasing trend. The drought trend in the study area detected based on SPEI was greater than the drought trend detected by SPI. The difference between SPEI and SPI in the winter wheat growing season was smaller than that in the spring wheat and maize growing seasons. 2) With the increase in EDD and the decrease of SPI/SPEI values, the probability of negative yield fluctuation gradually increased, and the probability of positive yield fluctuation gradually decreased. Under the same drought and high temperature conditions, the probability of yield fluctuation varies among different crops and different provinces. Drought has a greater impact on crop yield than high temperature. Both the single and interactive effects of drought and high temperature on yield are nonlinear. 3) Irrigation can effectively alleviate the impact of drought and high temperature on yield. In heavily irrigated provinces, the effects of both high temperature and drought on crop yield are not obvious.
... This issue can be better understood in Sub-Saharan Africa maize agro-ecosystems where Lobell et al. (2011) reported that maize yield is reduced 1 to 1.7% per each degree day above 30 °C. Other researchers reported that 1% increase in extreme-heat-degree-days and consecutive-dry-days led to maize yield losses of 0.2% and 0.07%, respectively, in China (Wei et al. 2017). In the Northeastern USA, it was also estimated that maize crops will experience greater frequencies (30 days) of daily high temperature above 35 °C during silking-anthesis and reproductive stages under RCP8.5 at the end of twenty-first century (Prasad et al. 2018). ...
Heat stress in combination with drought has become the biggest concern and threat for maize yield production, especially in arid and hot regions. Accordingly, different optimal solutions should be considered in order to maintain maize production and reduce the risk of heat stress under the changing climate. In the current study, the risk of heat stress across Iranian maize agro-ecosystems was analyzed in terms of both intensity and frequency. The study areas comprised 16 provinces and 24 locations classified into five climate categories: arid and hot, arid and temperate, semi-arid and hot, semi-arid and temperate, and semi-arid and cold. The impact of heat stress on maize under a future climate was based on a 5-multi-model ensemble under two optimistic and pessimistic emission scenarios (RCP4.5 and RCP8.5, respectively) for 2040-2070 using the APSIM crop model. Simulation results illustrated that in the period of 2040-2070, intensity and the frequency of heat stress events increased by 2.37 °C and 79.7%, respectively, during maize flowering time compared to the baseline. The risk of heat stress would be almost 100% in hot regions in the future climate under current management practices, mostly because of the increasing high-risk window for heat stress which will result in a yield reduction of 0.83 t ha-1. However, under optimal management practices,farmers will economically obtain acceptable yields (6.6 t ha-1). The results also indicated that the high-risk windows in the future will be lengthening from 12 to 33 days in different climate types. Rising temperatures in cold regions as a result of global warming would provide better climate situations for maize growth, so that under optimistic emission scenarios and optimal management practices, farmers will be able to boost grain yield up to 9.2 t ha-1. Overall, it is concluded that farmers in hot and temperate regions need to be persuaded to choose optimal sowing dates and new maize cultivars which are well adapted to each climate to reduce heat stress risk and to shift maize production to cold regions.
... A future climate change projection for the years between 2020 to 2050 was made and identified that climate change would pose a serious food security threat globally if it goes unchecked and during this same period the global maize demand is expected to increase by as much as 45 % (Chen et al., 2012;Zhai et al., 2017). In recent decades, extreme weather events caused by global warming have had adverse impacts on crop productivity, leading to a rise in food insecurity (Wei et al., 2016). According to The Food and Agriculture Organization (FAO), Africa and Asia are projected to be worst hit by a decline in agricultural production associated by climate change since the majority of the population are already affected by poverty and their over dependence on rainfed agriculture (FAO, 2009). ...
... However, in Shanxi province, frequent heat waves and droughts due to climate change have negatively impacted maize yields, as reported by Wei et al. (2016) and Zhao et al. (2016). On the other hand, a study by Zhao et al. (2015) has attributed the increase in maize yield in Northeast China to variations in solar radiation throughout the growth period of maize, which occurs from May to September. ...
According to multiple global circulation models and the Intergovernmental Panel on Climate Change (IPCC), it is expected that the planet's surface temperatures will persist in increasing, resulting in the occurrence of various weather calamities such as extreme precipitation, droughts, floods, and heat waves, all caused by climate change. The agricultural sector, which is primarily rainfed, is expected to be severely impacted by this climate variability. A study was conducted in Northeast China, Northwest China, and Nakuru County, Kenya to assess the impact of the anticipated climate variation on the production of spring maize and the efficacy of resilient strategies in significant agricultural systems. The study compared the two future periods of the 2030s (2021-2040) and the 2050s (2041-2060) with the baseline (1986-2005) using the DSSAT-CERES-Maize model under the RCP4.5 and RCP8.5 scenarios. The data analyzed included historical climate data (1986-2005), future climate data downscaled with the GCM-HadGEM2-ES model, crop, and soil data. In order to calibrate and validate the CERES-Maize crop model, a combination of observed weather data and future climate scenario data was utilized. The GCM-HadGEM2-ES model was employed to generate data for maize yield simulations in the baseline (1986-2005) as well as in the 2030s and 2050s for RCP scenarios (RCP4.5 and RCP8.5). Additionally, soil input data, including soil color, texture, particle size, organic carbon, pH, nitrogen levels, bulk density, type, cation exchange, and drainage, were collected from both Chinese and Kenyan soil scientific databases as well as local Agricultural Meteorological Experimental Stations (AMESs). Additionally, crop management practice input data were obtained from the AMESs in the study areas, which included selected sites in Northeast and Northwest China and Nakuru County, Kenya, for the period between 2005 and 2009. Chapter 3 of the investigation presents a clarification regarding the impact of forthcoming alterations in climate conditions on the production of spring maize in the northeastern region of China. The findings of the analysis revealed that projected annual average temperature, solar radiation, and precipitation would vary within the range of -2.21 to 3.85°C, -7.35 to 24.58%, and 6.06 to 25.24%, respectively, during the 2030s and 2050s under the RCP4.5 and RCP8.5 scenarios. Simulations were conducted using the CERES-Maize model v4.7 for the periods of 2030s and 2050s based on RCP4.5 and RCP8.5 climate scenarios, which revealed a reduction in maize yield by an average of 15.4% (2.4%-26.4%) and a shortening of the maize growth duration by an average of 15 days (1-38 days). Nonetheless, the outcomes of the simulation regarding measures for adaptation have indicated that with the execution of modifications in the schedule for planting, approaches for irrigation, and selection of cultivars, the average maize production could escalate by 33.7% (23.7% to 43.6%) for the periods of 2030s and 2050s under the RCP4.5 and RCP8.5 scenarios. In Chapter 4 of the research, the focus is on the impact of upcoming climate variations on the harvest of spring maize in Northwest China. As per the evaluation, it was observed that the estimated mean temperature, solar radiation, and rainfall for each year would alter between 0.48 to 3.26°C, -0.40 to -3.31%, and 2.34 to 54.31%, correspondingly, during the 2030s and 2050s while adhering to the RCP4.5 and RCP8.5 scenarios. By utilizing the CERES-Maize model v4.7, our simulation findings indicate that during the 2030s and 2050s, under the climate scenarios RCP4.5 and RCP8.5, the growth durations of maize are anticipated to decline by an average of 13 days (0-38 days). Consequently, the yield of maize may diminish by an average of 12.3% (4.6%-22.4%). Nonetheless, taking certain adaptation measures, such as adjusting planting dates, appropriate irrigation practices, and employing different cultivars, may boost the mean maize yield by an average of 23.1% (17.6%-28.6%) within the same timeframe. Chapter 5 of our research examines the consequences of potential climate changes on maize yield in Nakuru County, Kenya. According to our examination, it is predicted that the mean temperature, solar radiation, and rainfall in the upcoming decades of 2030s and 2050s would vary between 0.58 to 3.35°C, 1.15 to -8.05%, and 3.01 to 11.76%, respectively, under both RCP4.5 and RCP8.5 scenarios. As per the CERES-Maize model v4.7, the period of maize development is estimated to decline by an average of 11 days (0 to 34 days) within the same timeline. Consequently, this will lead to a drop in the maize harvest by an average of 12.8% (2.7% to 26.5%). Nonetheless, the implementation of adaptation measures, such as adjusting planting dates, using appropriate irrigation practices, and employing different cultivars, has the potential to increase maize yield by 29.7% (20.7% to 38.6%) under RCP4.5 and RCP8.5. In China and Kenya's study sites, the research revealed that late maturing maize cultivars cultivated at lower altitudes had longer growing seasons, allowing them to benefit from the warming temperatures and leading to increased yields. On the other hand, local early and medium maturing cultivars were adversely affected by the temperature increase, leading to reduced yields. To mitigate the effects of climate change on maize yield, effective adaptation measures include planting high temperature sensitive varieties earlier, altering the cultivar, and utilizing appropriate irrigation methods. These measures can help minimize the losses in yield due to climate change. Our study provides valuable insights into the impact of climate change on maize yield production in China and Kenya, and highlights the potential of adaptation measures to mitigate its effects. It is suggested that additional investigation should be conducted to examine the impact of diverse elements on maize harvests, which were not analyzed in this research, including but not limited to, the quality of the soil, the labor force, the prices in the market, and the levels of production within the locality. The findings of this research expand the existing scientific knowledge on the best adaptation measures to enhance maize yields in a changing climate, and thus are of significant importance.
... For example, a temperature rise of 1 • C above 35 • C could result in 1. .29% loss of maize yield [9], and maize productivity also decreases by 0.3% on cold days occurring during the growing season [10]. Each 1 • C of global warming (increase in temperature from 11 • C to 19 • C) decreases wheat yield by 4.1-6.4% [11]; and under cold stress (2 • C/8 • C) there is a 35-78% decrease in wheat grain yield [12]. Potatoes are sensitive to temperature changes too. ...
Cytosolic pH (pHcyt) regulates a wide range of cellular processes in plants. Changes in pHcyt occurring under the effect of different stressors can participate in signal transmission. The dynamics of pHcyt under the action of external factors, including significant factors for open ground crops such as temperature, remains poorly understood, which is largely due to the difficulty of intracellular pH registration using standard methods. In this work, model plants of potato (one of the essential crops) expressing a fluorescent ratiometric pH sensor Pt-GFP were created. The calibration obtained in vivo allowed for the determination of the pHcyt values of the cells of the leaves, which is 7.03 ± 0.03 pH. Cooling of the whole leaf caused depolarization and rapid acidification of the cytosol, the amplitude of which depended on the cooling strength, amounting to about 0.2 pH units when cooled by 15 °C. When the temperature rises to 35–40 °C, the cytosol was alkalized by 0.2 pH units. Heating above the threshold temperature caused the acidification of cytosol and generation of variation potential. The observed rapid changes in pHcyt can be associated with changes in the activity of H+-ATPases, which was confirmed by inhibitory analysis.
... Generally, increased warming has a tendency to accelerate the loss of water from soil, thereby affecting maize growth from germination to vegetative growth and later grain filling [40]. In a study in the Shanxi Province in China, [41] found that a one percent increase in extreme-heat-degree days and consecutive-dry days result in a maize yield decline of 0.2% and 0.07%, respectively. They found further that, if all the historical temperatures in the villages are shifted up by 2 degrees Celsius, the total impacts of these extreme events would lead to a reduction in maize yield by over 30%. ...
Agriculture is one of the sectors most susceptible to changes in climatic conditions. The impact is even stronger in Africa, where rain-fed agriculture is vital for daily subsistence, but where adaptive capacity is low. It is therefore crucial to increase the understanding of the actual climate change dynamics on agricultural productivity. This study examined the effects of changes in climatic variables such as rainfall and temperature on maize production in the Ejura-Sekyedumase Municipality, Ghana. Regression, chi-square and trend analyses were used to establish the relationship between climate variables (rainfall and temperature) and maize yield in the study area. This was supplemented with participatory household interviews with 120 farmers to understand the perception of farmers on rainfall and temperature patterns. The results from the study respondents and trend analysis show that rainfall is shorter in terms of duration and less predictable, whilst temperature has increased. The findings suggest that the general relationship between rainfall, temperature and maize yield is such that maize yield increased with increasing rainfall of the right amount and distribution pattern and decreased with increasing temperature. The study concludes that climate variability and/or change is evident in the study area and its effect on maize yield is severe.
... HT impact on maize crop yield is a major concern among the scientific community. Taoyuan et al. [103] statistically estimated the impact of heat waves (and other extreme events) on maize yield, based on household survey data from 1993 to 2011 in ten villages of Shanxi province, China, demonstrating marginal yield declines for the study period. However, in climate change conditions as derived by six climate models and two future climate forcing scenarios, Hong [104] presented a decrease in maize yield ranging from 15 to 50%. ...
In deltaic areas, riverine and coastal waters interact; hence, these highly dynamic environments are particularly sensitive to climate change. This adds to existing anthropogenic pressures from irrigated agriculture, industrial infrastructure, urbanization, and touristic activities. The paper investigates the estimated future variations in the dynamics of surface and coastal water resources at a Mediterranean deltaic environment for the twenty-first century. Therefore, an Integrated Deltaic Risk Index (IDRI) is proposed as a vulnerability assessment tool to identify climate change impact (CCI) on the study area. For this purpose, three regional climate models (RCM) are used with representative concentration pathways (RCPs) 4.5 and 8.5 for short-term (2021–2050) and long-term (2071–2100) future periods. Extensive numerical modeling of river hydrology, storm surges, coastal inundation, water scarcity, and heat stress on irrigated agriculture is combined with available atmospheric data to estimate CCI on the Nestos river delta (Greece). The IDRI integrates modeling results about (i) freshwater availability covering agricultural demands for three water consumption scenarios, i.e., a reference (REF), a climate change (CC), and an extended irrigation (EXT) scenario, combining river discharges and hydropower dam operation; (ii) inundated coastal areas due to storm surges; and (iii) heat stress on cultivated crops. Sustainable practices on irrigated agriculture and established river basin management plans are also considered for the water demands under combinatory scenarios. The differentiations of model outputs driven by various RCM/RCP combinations are investigated. Increased deltaic vulnerability is found under the RCP8.5 scenario especially for the long-term future period. The projected IDRI demonstrates the need for integrated water resources management when compared with risk indexing of individual water processes in the study area.
... The sixth low yield event of 2009-2010 had after-effects of flood in 2008 due to a breach of the embankment in Nepal [75]. As already discussed in Section 1, Bihar is a rainfed region and rainfed regions of India are affected by extreme hydrological events like some other exceptional regions across the globe [76,77]. Another cause for the decline was indicated by ONI (JJA of 2009 to JJA of 2009) and SOI (MAM 2009 to JJA 2009) as shown in Table 1. ...
The impact of Indo-Pacific climate variability in the South Asian region is very pronounced and their impact on agriculture is very important for the Indian subcontinent. In this study, rice productivity, climatic factors (Rainfall, Temperature and Soil Moisture) and associated major Indo-Pacific climate indices in Bihar were investigated. Bihar is one of the major rice-producing states of India and the role of climate variability and prevailing climate indices in six events (between 1991-2014) with severer than −10% rice productivity are analyzed. The Five-year moving average, Pearson's Product Moment Correlation, Partial Correlation, Linear Regression Model, Mann Kendall Test, Sen's Slope and some other important statistical techniques were used to understand the association between climatic variables and rice productivity. Pearson's Product Moment Correlation provided an overview of the significant correlation between climate indices and rice productivity. Whereas, Partial Correlation provided the most refined results on it and among all the climate indices, Niño 3, Ocean Niño Index and Southern Oscillation Index are found highly associated with years having severer than −10% decline in rice productivity. Rainfall, temperature and soil moisture anomalies are analyzed to observe the importance of climate factors in rice productivity. Along with the lack of rainfall, lack of soil moisture and persistent above normal temperature (especially maximum temperature) are found to be the important factors in cases of severe loss in rice productivity. Observation of the dynamics of ocean-atmosphere coupling through the composite map shows the Pacific warming signals during the event years. The analysis revealed a negative (positive) correlation of rice productivity with the Niño 3 and Ocean Niño Index (Southern Oscillation Index).
