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An accurate analysis of climate extremes is essential for impact assessment and devising appropriate adaptation measures. There is an urgent need to assess precipitation products in capturing the increasing occurrence of climate extremes. This study evaluates the ability of 20 observational datasets, including gauge-based, satellite-based and reanalyses, in representing different drought severity (moderate, severe and extreme drought) over Africa and its nine sub-regions at varying time scales (3-, 6- and 12-months) during 1983–2014. Drought is represented using the Standardized Precipitation Index (SPI). The results demonstrate that while most datasets are suitable for drought studies over the continent, the African Rainfall Climatology version 2 (ARC2) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN_CDR_v1r1) are less fitted for such investigations. Moreover, regions such as the Sahara (SAH), Central Africa (CAF) and North Eastern Africa (NEAF) show a larger disparity among the datasets, requiring more caution when selecting a dataset for use in such areas. Generally, the datasets present low agreement toward the lower end of the range (5–30%) because the individual datasets estimate varying drought severities at different grids and months. This is observed in the coefficient of variation of 20–25% of the datasets falling outside the ± 1 standard deviation range. Therefore, using an ensemble to represent the datasets remains an indispensable tool. The datasets present better agreement in the timing of drought events than the spatial distribution. The findings provide valuable insights into the complexity of drought assessment using diverse precipitation datasets. Furthermore, the results highlight the significance of considering spatial and temporal dimensions, as datasets may capture drought events at varying locations and times, revealing subtle variations in drought impact. [ABSTRACT FROM AUTHOR]

Quantifying the vulnerability of population to multi-faceted climate change impacts on human well-being remains an urgent task. Recently, weather and climate extremes have evolved into bivariate events that heighten climate risks in unexpected ways. To investigate the potential impacts of climate extremes, this study analyzes the frequency, magnitude, and severity of observed and future compound hot-dry extremes (CHDEs) over East Africa. The CHDE events were computed from the observed precipitation and maximum temperature data of the Climatic Research Unit gridded Timeseries version five (CRU TS4.05) and outputs of climate models of Coupled Model Intercomparison Project Phase 6 (CMIP6). In addition, this study quantifies the population exposure to CHDE events based on future population density datasets under two Shared Socioeconomic Pathways (SSPs). Using the 75th/90th and 25th/10th percentile of precipitation and temperature as threshold to define severe and moderate events, the results show that the East African region experienced multiple moderate and severe CHDE events during the last twenty years. Based on a weighted multi-model ensemble, projections indicate that under the SSP5-8.5 scenario, the frequency of moderate CHDE will double, and severe CHDE will be 1.6 times that of baseline (i.e., an increase of 60%). Strong evidence of an upward trajectory is noted after 2080 for both moderate and severe CHDE. Southern parts of Tanzania and northeastern Kenya are likely to be the most affected, with all models agreeing (signal-to-noise ratio, SNR > 1), indicating a likely higher magnitude of change during the mid- and far-future. Consequentially, population exposure to these impacts is projected to increase by up to 60% for moderate and severe CHDEs in parts of southern Tanzania. Attribution analysis highlights that climate change is the primary driver of CHDE exposure under the two emission pathways. The current study underscores the urgent need to reduce CO2 emissions to prevent exceeding global warming thresholds and to develop regional adaptation measures. [ABSTRACT FROM AUTHOR]

Over the past decades, drought has been considered as one of the most catastrophic natural disasters due to the devastating implications associated with this phenomenon. This research examines drought characteristics, severity, intensity, and duration in Sudan from 1980 to 2017 using meteorological observations and remote sensing datasets. According to analyses of the standardized precipitation index (SPI) provided by 29 sites, 72% of all dry occurrences appear to have occurred during the 1980s. The wettest occurrences, on the other hand, occurred after the mid-1990s timeframe and accounted for 82% of the total. SPI-3 research over Sudan revealed dry conditions during the 1980s. Early datasets indicate extreme drought periods occurred between 1982 and 1987, while fewer occurrences were observed during the 1990s and 2000s. SPI-3 shows that rainfall increased dramatically in the 1990s. Among the understudied time scales, 1982, 1983, 1984, 1987, 1990, 1992, and 2004 regimes recorded the driest events, whereas 1988, 1998, 1999, 2000, 2003, 2005, 2006, 2007, and 2016 had the wettest. Recent seasonal vegetation conditions from the normalized difference vegetation index (NDVI) and soil moisture were examined. The intensity of severe droughts declined for 2000, 2005, 2010, 2015, and 2020 periods. Conversely, a discernible upward trend in wet conditions with corresponding increases can be observed. Findings depict a graphical illustration of changes in the vegetation condition index (VCI), both geographically and chronologically in Sudan between 2000 and 2020. Generally, a positive correlation between the SPI-12 and AMO was obtained across Sudan's different regions. Significant power association peaked between 1 and 2 and 6 and 10 years from 1980 to 1990 and 6 and 8 years from 2005 to 2017. This study lays the groundwork for future drought studies, which further provides the technical basis for policymakers to address the consequences of drought on agriculture and the economy. [ABSTRACT FROM AUTHOR]

This study examines seasonal and annual mean temperature changes from 1970 to 2014. Climatologically, the June–July–August season exhibited the highest (27 °C) mean temperatures over the country, followed by March–April–May (MAM; 20.2 °C), September–October–November (SON; 19.1 °C), and December–January–February (DJF; 8.8 °C), while the annual mean was 18.8 °C. The southern region exhibited higher mean temperatures than the northern region for the DJF, MAM, JJA, SON, and annual timescales during the study period. The seasonal trend across the country was highest in MAM (0.027 °C/year), followed by SON (0.025 °C/year), DJF (0.023 °C/year), and JJA (0.016 °C/year). The interannual trend increased significantly at 0.023 °C/year across the country. Across the north, MAM showed the highest increase (nonsignificant) in trends at 0.025 °C/year, followed by a significant increase during SON (0.018 °C/year), DJF (0.017 °C/year), and JJA (0.010 °C/year), while annual trends were the lowest (0.017 °C/year). Examination of abrupt change over Pakistan showed nonsignificant change during JJA, while MAM, JJA, SON, and annual timescales demonstrated significant positive and negative changes. Decadal anomalies showed long-term positive tendencies in DJF (since 1990–2014) temperature, followed by JJA, SON, annual, and MAM timescales (2000–2014). In conclusion, the observed changes in temperature were significantly robust and promise increasing signal patterns in all time scales. [ABSTRACT FROM AUTHOR]

Pakistan is facing an increasing challenge of extreme climate-related events with accelerating global warming. Accurate simulation and reliable projection of change in temperature properties under climate change is important for developing appropriate adaptation strategies. The present study assessed the performance of bias-corrected CMIP6 models in representing seasonal and annual surface air temperatures (tas) over Pakistan. The results reveal that the majority of models reasonably capture the dominant features of the spatial variations in observed temperature, despite the warm (cold) bias over northern (southern) parts of Pakistan. However, employing the quantile mapping algorithm significantly reduced biases in models. The projections in near-future (2015–2039), mid-century (2040–2069), and late-century (2070–2099) temperatures under SSP2-4.5 and SSP5-8.5 scenarios revealed diverse changes. Significant warming tendencies under SSP5-8.5 scenarios in winter (1.92 °C/decade), annual (0.85 °C/decade), and summer (0.84 °C/decade) are expected in the twenty-first century. The late-century winters project strong mean changes of 5.2 °C under SSP5-8.5, with mean changes reaching nearly 3.0 to 6.5 °C in northern parts of the country. Late-century summers projected mean changes of 4.3 °C under SSP5-8.5, with the strongest change of 2.5–6.1 °C exhibited at the northern and southwestern parts. The late-century annual temperature may show changes at 5.0 °C under SSP5.8–5, especially over northwestern regions projected to exhibit mean changes of 5.0 to 5.5 °C. The study highlights the robust warming towards the end of the century and thus calls for urgent demand to regulate the emissions toward reducing impacts in the future over the study region. [ABSTRACT FROM AUTHOR]

The ongoing global warming has caused unprecedented changes in the climate system, leading to an increase in the intensity and frequency of weather and climate extremes. This study uses the sixth phase of Coupled Model Intercomparison Project (CMIP6) data to investigate projected changes in drought events over East Africa (EA) under four Shared Socioeconomic Pathway (SSP) emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-4.0, and SSP5-8.5). The CMIP6 data are bias-corrected using a quantile mapping method, with the Climatic Research Unit's precipitation dataset as reference. Drought is quantified using the standardized precipitation index and different measures of drought are estimated: drought duration, drought frequency, drought severity, and drought intensity. Evaluating the accuracy and reliability of historical data before and after bias correction demonstrates the importance of the approach. The overall distribution after bias correction depicts a close agreement with observation. Moreover, the multi-model ensemble mean demonstrate superiority over individual Global Circulation Models. Projected future changes show enhancement in precipitation over most parts of EA in the far future under different SSP scenarios. However, the arid and semi-arid regions are expected to receive less amount of precipitation, whereas the highlands and lake regions are expected to receive a larger amount of precipitation increase. Furthermore, the dry areas of EA are likely to experience more frequent drought events with longer duration, stronger intensity, and severity in the far future. Overall, this study identifies possible drought hotspots over EA, enabling early preparation for such events. [ABSTRACT FROM AUTHOR]

Information about likely future patterns of climate variables is important in climate change mitigation and adaptation efforts. This study investigates future (2021–2100) changes in rainfall based on CMIP6 datasets over Uganda. The projection period is divided into two sub-periods: 2021–2060 (near future) and 2061–2100 (far future), relative to the baseline period (1985–2014). Two emission scenarios: SSP2-4.5 and SSP5-8.5, are considered. The results reveal a larger decrease (increase) in rainfall during March–April (November–December) under both SSPs. Moreover, an enhanced decline (increase) is projected under SSP2-4.5 (SSP5-8.5). The spatial distribution of future changes in seasonal rainfall reveals a decrease in MAM rainfall in the near future over most parts of the country under both emission scenarios. However, a recovery is exhibited towards the end of the century with more increase in the south-western parts of the country, and a higher magnitude under SSP5-8.5. In contrast, SON rainfall reveals wetter conditions during both timelines and emission scenarios. Maximum (minimum) wet conditions are expected in the north-western parts of the country (around the Lake Victoria basin). The linear trend analysis shows a non-significant (z = − 0.714) decreasing trend for MAM rainfall during the historical period. This pattern is reflected in the near future with z-scores of − 0.757 and − 1.281 under SSP2-4.5 and SSP5-8.5, respectively. However, a significant increase for MAM and annual rainfall (z-scores of 2.785 and 3.46, respectively) is projected towards the end of the century under SSP5-8.5. These findings provide guidance to policy makers in devising appropriate adaptation measures to cope with expected changes in the local climate. Given the increase in intensity and frequency of extreme rainfall over the study region, future work should focus on examining projected changes in rainfall extremes under different global warming scenarios with consideration of model performance and independence. [ABSTRACT FROM AUTHOR]

East African countries (Uganda, Kenya, Tanzania, Rwanda, and Burundi) are adversely impacted by droughts and floods which occasionally result from rainfall variability and periodicity. For this study, rainfall datasets from 30 rainfall stations were selected from the eight homogeneous rainfall zones of East Africa (EA) after preliminary quality assessment and tests, including outliers, normality, and homogeneity tests. The Morlet wavelet technique was used to establish the periodicity of monthly rainfall for typical March–May (MAM) and October–December (OND) seasons. Mann–Kendall's (MK) statistical test and Sen's slope estimator (SE) were used to examine the trend and the magnitude of change in seasonal and annual rainfall over EA, respectively. Wavelet transform approaches were used to show the linkages between the seasonal rainfall and the two atmospheric indices: El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Results revealed a 1-year band as a dominant period of variability over EA. MK results indicate that 97% of the stations had a decreasing trend during MAM rainfall seasons, while nearly 60% of the stations presented a positive trend for OND and annual rainfall. A statistically significant negative correlation was established between ENSO and MAM rainfall, indicating a strong link between ENSO/La Niña and drought events during 1973/1974 and 1983/1984. Meanwhile, a statistically significant positive correlation between IOD and OND seasonal rainfall coincided with the 1972/1973 and 1997/1998 El Niño events. This established relationship could be used in drought and flood early warning systems to predict extreme events like drought and floods over EA. Besides, for the highly variable rainfall in the region, tailor-made climate information for planning economic programs like agriculture, energy, and disaster preparedness is highly recommended. [ABSTRACT FROM AUTHOR]

Understanding rainfall variability over a region is essential for economic development since it contributes to managing climate-sensitive sectors. This study assesses the interannual characteristics of rainfall over Madagascar and the possible influence of the Indian Ocean (IO) on rainfall patterns between 1950 and 2018, using monthly rainfall data from the Climatic Research Unit. The singular value decomposition analysis was used to examine the effect of IO's sea surface temperature (SST) on the rainfall variability, followed by a composite analysis of vertically integrated moisture flux. Overall, both the wet and dry seasons showed a slight decrease in rainfall trends during the past 69 years. During the wet (dry) season, the central and southeastern (western) IO SST were positively correlated with Madagascar's rainfall. The covariability analysis showed that a positive phase of the Subtropical Indian Ocean Dipole or Indian Ocean Dipole results in increased rainfall over the northwestern (northern) and southern parts of Madagascar during the wet (dry) season. The composite analysis suggests that enhanced (decreased) rainfall during the wet (dry) years of the wet and dry seasons in Madagascar is linked to a strong moisture convergence (divergence) accompanied by strong easterlies (anticyclonic circulation) over the northwestern (southern) IO. The study findings could drive further studies on other likely factors that impact the country's rainfall. [ABSTRACT FROM AUTHOR]

This review study examines the state of meteorological drought over Africa, focusing on historical trends, impacts, mitigation strategies, and future prospects. Relevant meteorological drought-related articles were systematically sourced from credible bibliographic databases covering African subregions in the twentieth and twenty-first centuries (i.e. from 1950 to 2021), using suitable keywords. Past studies show evidence of the occurrence of extreme drought events across the continent. The underlying mechanisms are mostly attributed to complex interactions of dynamical and thermodynamical mechanisms. The resultant impact is evidenced in the decline of agricultural activities and water resources and the environmental degradation across all subregions. Projected changes show recovery from drought events in the west/east African domain, while the south and north regions indicate a tendency for increasing drought characteristics. The apparent intricate link between the continent's development and climate variability, including the reoccurrence of drought events, calls for paradigm shifts in policy direction. Key resources meant for the infrastructural and technological growth of the economy are being diverted to develop coping mechanisms to adapt to climate change effects, which are changing. Efficient service delivery to drought-prone hotspots, strengthening of drought monitoring, forecasting, early warning, and response systems, and improved research on the combined effects of anthropogenic activities and changes in climate systems are valuable to practitioners, researchers, and policymakers regarding drought management in Africa today and in the future. [ABSTRACT FROM AUTHOR]

Maize crop (Zea mays) is one of the staple foods in the East African (EA) region. However, the suitability of its production area is threatened by projected climate change. The Multimodel Ensemble (MME) from eight Coupled Model Intercomparison Project 5 (CMIP5) models was used in this paper to show climate change between the recent past (1970–2000) and the future (2041–2060), i.e., the mid-twenty-first century. The climatic suitability of maize crop production areas is evaluated based on these climate datasets and the current maize crop presence points using Maximum entropy models (MaxEnt). The MME projection showed a slight increase in precipitation under both RCP4.5 and RCP8.5 in certain places and a reduction in most of southern Tanzania. The temperature projection showed that the minimum temperature would increase by 0.3 to 2.95 °C and 0.3 to 3.2 °C under RCP4.5 and 8.5, respectively. Moreover, the maximum temperature would increase by 1.0 to 3.0 °C and 1.2 to 3.6 °C under RCP4.5 and 8.5 respectively. The impacts of these projected changes in climate on maize production areas are the reduction in the suitability of the crop, especially around central and western Tanzania, mid-northern and western Uganda, and parts of western Kenya by 20–40%, and patches of EA will experience a reduction of as high as 40–60%, especially in northern Uganda, and western Kenya. The projected changes in temperature and precipitation present a significant negative change in maize crop suitability. Thus, food security and the efforts towards the elimination of hunger in EA by the mid-twenty-first century will be hampered significantly. We recommend crop diversification to suit the new future environments, modernizing maize farming programs through the adoption of new technologies including irrigation, and climate-smart agricultural practices, etc. [ABSTRACT FROM AUTHOR]

During recent decades, Burundi was threatened by recurrent dry events resulting in crop failure and more socio-economic losses. To assess the drought occurrence, duration and intensity, this study used the Standardized Precipitation Index (SPI) at 3-, 6- and 12-month time scales for the period 1981–2017. The Mann Kendall and Modified Mann Kendall trend tests and Sen's slope statistic test were used to analyse the spatiotemporal drought trend. The overall analysis of SPI-3, SPI-6 and SPI-12 results revealed that the northern part of Burundi was the most threatened by dry events, where more than 80% of the extremely and severely dry events occurred within the period 1993–2000. The drought magnitude highly varied during the short rains season (SOND) specifically during the 1990s decade. The cumulative frequency of extremely dry events was very high in the North with 5.2%, 6.1% and 7.4% at 3-, 6- and 12-month time scales respectively. Likewise, the northern part experienced short, medium and long dry periods, thus 88 consecutive dry months within only 8 years. The North and East regions exhibited a positive increasing trend over annual and seasonal time scales at 3, 6 and 12 months while the mountainous region and the South experienced a significant decreasing trend. Investigation of teleconnection factors showed that dry events are associated with sea surface temperature anomalies over the Indian Ocean resulting in the weakening of southeasterly winds during the negative phase of Indian Ocean Dipole and La Nina years. [ABSTRACT FROM AUTHOR]

The lack of reliable rainfall projection records remains a major challenge to Uganda. In the advent of extreme wetness or drought events, reliable rainfall estimates for local planning and adaptation are essential. The present study used two main datasets to conduct a historical analysis from 1981 to 2019, coupled with future projections under representative concentration pathway (RCP 8.5) for the period 2020–2050. Historical analysis revealed bimodal annual rainfall patterns for March–May (MAM) and September–November (SON) gradients representing heavier to lighter rainfall events, respectively, over the study area. Investigation of recent trends in rainfall patterns revealed an upward trend from 2010 onwards in annual and seasonal rainfall. Moreover, results for future projections show wet conditions are projected to occur over the study area between the months of April/May and October. Contrarily, March is likely to experience a reduction in wet conditions. Mann–Kendall test employed to make future projections of rainfall depicted decreasing patterns during MAM season whilst increasing tendencies with strong shift was highlighted for SON season over the study region. Meanwhile, annual projections indicate huge variations with linear trends showing a marginal increase as compared to historical trends. Findings would serve as baseline print to propel further studies that could delve into impact analysis of drought extreme events which pose significant threats to the agricultural sector which is heavily reliant on rainfall. [ABSTRACT FROM AUTHOR]

Drought is a recurrent phenomenon in the Sudano–Sahelian region of Cameroon. However, it has received very little attention, especially, on its impacts on the growing season of crops. To fill this gap, this study assessed the drought hazard using standardized precipitation index at a 3-month scale, and phenology of the main crops in 19 rainfall stations in the Sudano–Sahelian region of Cameroon for the period 1980–2012. The trend of drought was studied using the Mann-Kendall technique. The agricultural drought hazard was quantified based on its frequency and intensity. Results show that there is a significant trend toward a more humid crop-growing period in the northern and southeastern parts of the study area, but significant dry trends during the maize and peanut growing periods in the southwestern part of the study area. Drought occurrence rates are 7.09, 4.22, and 3.17% for the moderate, severe, and extreme, respectively. Areas featuring high and very high agricultural drought hazards are distributed in the far north, central, and the southeastern parts of the study domain. Furthermore, the Sahelian and Sudanian climatic conditions were found to be very high agricultural drought hazard zones, especially, where the maize and peanut grain crops are concerned. However, significant dry trends and very high agricultural drought hazard zones during the maize and peanut growing periods are catastrophic for agriculture and, therefore, food security. [ABSTRACT FROM AUTHOR]

Diurnal temperature range (DTR) is an important index in climate change studies in addition to its influence upon environment and thermal comfort. Understanding variability in DTR at regional scales is, thus, important. In Fiji and other Pacific Island countries, DTR information is important in forecasting thermal comfort. This work is based on Fiji using gauge-based gridded mean monthly DTR data from the Climatic Research Unit (CRU). Annual and monthly DTR for the second half of the twentieth century to the present day are analyzed to establish temporal trends and spatial patterns. A combination of parametric and non-parametric tests was applied to investigate trend, correlation, simple linear regression, and interannual variability in the datasets. Findings show that DTR increases with an increase in latitude over Fiji. The mean monthly DTR between 6.58 and 7.37 °C in June and January coincide with winter and summer, respectively. Cumulative annual mean (CAM) of T, Tmx, and DTR showed increasing trends during the study period while the CAM of Tmn depicted a decreasing trend. Results suggest that DTR has a positive significant (insignificant) correlation with Tmx (T), but shows an opposing significant relationship with Tmn at 5% significance level. Each of DTR, Tmx, Tmn, and T experienced a general increase in values across the timeframe provided by the data. Records show an overall increase of 0.05 °C/decade in DTR. However, since the early 1990s, DTR has been characterized by a downward trend. Nonetheless, the overall trend of increasing DTR is explained by a greater increase in maximum temperatures over minimum temperatures. The observed rate of increase in DTR in warm months exceeds that in cold months. The findings form a baseline for further studies investigating the factors influencing DTR variability, and how the variability is affecting human thermal comfort. [ABSTRACT FROM AUTHOR]

Global warming impacts leading to climate change is influencing all aspects of life and the ecosystems. Paris agreement capped the increase to a target below 2 °C and further downward trajectory to 1.5 °C above pre-industrial levels. This resolution requires nations to progressively monitor recent past and current temperature trends for accurate projections and adaptations. The present study characterizes recent temporal evolution of surface air temperature over Kenya during 1971–2010. The study utilized Climatic Research Unit reanalysis datasets for monthly minimum, maximum and mean average temperatures. Linear regression is utilized in determining statistical significance of trends, while Sequential Mann–Kendall rank test is employed in evaluating abrupt changes in temperature during the study period. Spatial analysis of mean temperature during the last four decades reveals evidence of rising temperature on annual and seasonal bases. Positive trends dominate western and central parts of Kenya, whereas insignificant positive trends are observed over northern and eastern parts of the study domain. Similarly, linear trends exhibit rising temperatures at the rate of 0.09 °C/decade, with an abrupt change in annual mean being detected in 1992. Overall, significant increase at 99% confidence level is observed during March–May season, while 95% confidence level is witnessed during October–December season. [ABSTRACT FROM AUTHOR]

East African countries (Uganda, Kenya, Tanzania, Rwanda, and Burundi) experience extreme rainfall phenomena such as droughts, floods, or wet events that usually impact negatively on agriculture. In this regard, the Standardized Precipitation Evapotranspiration Index (SPEI) is employed to assess the occurrence of agricultural drought and wet events based on 3-month and 12-month intervals over the period 1981–2017. Changes in maize yields with rainfall and temperature are analyzed using multi-regression techniques after detrending. The Modified Mann–Kendall test (m-MK) was used to examine the trends in yields and maize production area. The sensitivity index of maize crop yield (MCSI) is expressed as a reduction in maize yields/harvest caused by climate variations or extreme events. The results revealed higher frequencies of drought and wet events in a 3-month SPEI, while a 12-month SPEI presented more distinctive drought and wet events. Most agricultural regions in East Africa (EA) experienced between 43 and 47 months of moderate drought events, 20–23 months of severe droughts, and 11–9 months of extreme drought events during the period 1981–2017. Meanwhile, the wet events of 1997/1998, and 2015/2016 were fully revealed by both 3-month and 12-month SPEI, showing the robustness of SPEI in capturing drought and wet conditions over EA. Statistical crop models revealed that climate variability explained maize yield by approximately 63.0%, 57.0%, 60.0%, 56.0%, and 50.0% for Uganda, Kenya, Tanzania, Rwanda, and Burundi respectively. Except for Rwanda, m-MK showed that maize yields were statistically significantly increasing at a rate of 217.1, 88.9, 143.0, 52.0, and 53.0 kg/ha/year for Uganda, Kenya, Tanzania, and Burundi respectively. MCSI value of 1.0 was revealed in rainfall zone 4, and 0.95 for rainfall zone 5, as compared to 0.85 reported for each of rainfall zones 3 and 8. This implies that the former rainfall zones are more susceptible to extreme drought events or variations than the latter zones. Measures such as irrigation, rainwater harvesting, early warning systems for drought and wet events, and post-harvest handling of crops utilizing drying plates are recommended to adapt to the effects of climate variability. [ABSTRACT FROM AUTHOR]