BACKGROUND: Kinabalu Park, in Sabah (Malaysia) on Borneo Island, is renowned for the exceptionally high plant diversity it protects, with at least 5000 plant species enumerated to date. Discoveries of plant novelties continue to be made in Sabah, especially on isolated ultramafic outcrops, including in the genus Pittosporum (Pittosporaceae) with P. linearifolium from Bukit Hampuan on the southern border of the Park, and P. silamense from Bukit Silam in Eastern Sabah, both narrow endemics restricted to ultramafic soils.
RESULTS: A distinctive new species of Pittosporum (P. peridoticola J.B.Sugau and Ent, sp. nov.) was discovered on Mount Tambuyukon in the north of Kinabalu Park during ecological fieldwork. The diagnostic morphological characters of this taxon are discussed and information about the habitat in which it grows is provided. The soil chemistry in the rooting zone of P. peridoticola has high magnesium to calcium quotients, high extractable nickel and manganese concentrations, but low potassium and phosphorus concentrations, as is typical for ultramafic soils. Analysis of foliar samples of various Pittosporum-species originating from ultramafic and non-ultramafic soils showed a comparable foliar elemental stoichiometry that is suggestive of 'Excluder-type' ecophysiology.
CONCLUSION: Pittosporum peridoticola is an ultramafic obligate species restricted to Kinabalu Park with only two known populations within the boundaries of the protected area. It is vulnerable to any future stochastic landscape disturbance events, such as forest fires or severe droughts, and therefore its conservation status is 'Near Threatened'.
Wheat is an important global staple food crop; however, its productivity is severely hampered by changing climate. Erratic rain patterns cause terminal drought stress, which affect reproductive development and crop yield. This study investigates the potential and zinc (Zn) and silicon (Si) to ameliorate terminal drought stress in wheat and associated mechanisms. Two different drought stress levels, i.e., control [80% water holding capacity (WHC) was maintained] and terminal drought stress (40% WHC maintained from BBCH growth stage 49 to 83) combined with five foliar-applied Zn-Si combinations (i.e., control, water spray, 4 mM Zn, 40 mM Si, 4 mM Zn + 40 mM Si applied 7 days after the initiation of drought stress). Results revealed that application of Zn and Si improved chlorophyll and relative water contents under well-watered conditions and terminal drought stress. Foliar application of Si and Zn had significant effect on antioxidant defense mechanism, proline and soluble protein, which showed that application of Si and Zn ameliorated the effects of terminal drought stress mainly by regulating antioxidant defense mechanism, and production of proline and soluble proteins. Combined application of Zn and Si resulted in the highest improvement in growth and antioxidant defense. The application of Zn and Si improved yield and related traits, both under well-watered conditions and terminal drought stress. The highest yield and related traits were recorded for combined application of Zn and Si. For grain and biological yield differences among sole and combined Zn-Si application were statistically non-significant (p>0.05). In conclusion, combined application of Zn-Si ameliorated the adverse effects of terminal drought stress by improving yield through regulating antioxidant mechanism and production of proline and soluble proteins. Results provide valuable insights for further cross talk between Zn-Si regulatory pathways to enhance grain biofortification.
Drought monitoring is crucial for assessing and mitigating the impacts of water scarcity on various sectors and ecosystems. Although traditional drought monitoring relies on soil moisture data, remote sensing technology has have significantly augmented the capabilities for drought monitoring. This study aims to evaluate the accuracy and applicability of two temperature vegetation drought indices (TVDI), TVDINDVI and TVDIEVI, constructed using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) vegetation indices for drought monitoring. Using Guangdong Province as a case, enhanced versions of these indices, developed through Savitzky-Golay filtering and terrain correction were employed. Additionally, Pearson correlation analysis and F-tests were utilized to determine the suitability of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) in correlation with TVDINDVI and TVDIEVI. The results show that TVDINDVI had more meteorological stations passing both significance test levels (P
Drought stress is the main abiotic factor affecting rice production. Rain-fed upland rice which is grown on unbounded fields and totally dependent on rainfall for moisture is more prone to drought stress compared to rice from other ecosystems. However, upland rice has adapted to this limited water condition, thus are more drought tolerant than rice from other ecosystems. We performed the first transcriptome sequencing of drought tolerant indica upland rice cultivar Kuku Belang to identify differentially expressed genes related to drought tolerance mechanism. Raw reads for non-treated and PEG-treated Oryza sativa subspecies indica cv. Kuku Belang were deposited in the NCBI SRA database with accession number SRP074520 (https://www.ncbi.nlm.nih.gov/sra?term=SRP074520).
Rice is an important staple crop produced and consumed worldwide. However, poor seed emergence is one of the main impediments to obtaining higher yield of rice especially in hot and dry ecosystems of the world that are ravaged by drought. Therefore, this study was carried out to evaluate the effects of potassium nitrate (KNO3), salicylic acid (SA) and silicon dioxide (SiO2) priming in improving emergence, seedling growth, biochemical attributes and antioxidant activities of FARO44 rice under drought conditions. Rice seedlings primed with 2.5% and 5% KNO3, 3% and 3.5% SiO2, and 1 mM and 2.5 mM SA were subjected to three drought levels of low, moderate and severe under the greenhouse. Seed emergence, seedling growth, biochemical attributes and antioxidant activities were thereafter evaluated. Seed priming experiments were laid in a completely randomized design with five replicates per treatment. The results found that rice seedlings responded differently to different priming treatments. However, all primed rice seedlings had significantly (P ≤ 0.05) improved emergence percentage (72-92%), seedling growth, seedling vigor, seedling fresh and dry biomass and shorter emergence time compared with controls. Likewise, total soluble protein content, activities of catalase, ascorbate peroxidase and superoxide dismutase, carbohydrate, soluble sugar and total chlorophyll contents of rice seedlings were increased by more than two-folds by seed priming compared with control. Salicylic acid showed less effect in increasing emergence, seedling growth, antioxidant activities and biochemical attributes of rice. Thus, this study established that seed priming with KNO3 (2.5% and 5%) and SiO2 (3% and 3.5%) were more effective in improving emergence, seedling growth, biochemical attributes and antioxidant activities of FARO44. Thus, priming of FARO44 rice with this chemical is recommended for fast emergence, seedling growth and drought resistance in dry ecosystems.
The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.
In 1877, the major towns of the Straits Settlements-Singapore, George Town, Penang Island and Malacca-suffered a drought of exceptional magnitude. The drought's natural instigator was the El Niño phase of the El Niño Southern Oscillation, a climatic phenomenon then not understood by contemporary observers. The 1877 event has been explored in some depth for countries including India, China and Australia. Its impact on Southeast Asia however is less well-known and the story of how the event unfolded in Singapore and Malaysia has not been told. This paper explores how the contemporary British government responded to the drought, arguing that its impact on hydraulic management was at best minimal yet, it did have impact on other areas, such as forest reservation with the hope of preserving future rainfall. It also highlights how, in contrast to studies on urban water plans in other British Asian colonies, the colonial authorities in the Straits Settlements had a far less coherent and meaningful relationship with water in their town planning schemes. As this paper is part of a special issue, Water History in the time of COVID-19, it has undergone modified peer review.
Developing drought-tolerant rice varieties with higher yield under water stressed conditions provides a viable solution to serious yield-reduction impact of drought. Understanding the molecular regulation of this polygenic trait is crucial for the eventual success of rice molecular breeding programmes. microRNAs have received tremendous attention recently due to its importance in negative regulation. In plants, apart from regulating developmental and physiological processes, microRNAs have also been associated with different biotic and abiotic stresses. Hence here we chose to analyze the differential expression profiles of microRNAs in three drought treated rice varieties: Vandana (drought-tolerant), Aday Sel (drought-tolerant) and IR64 (drought-susceptible) in greenhouse conditions via high-throughput sequencing.
Drought and submergence have been the major constraint in rice production. The present study was conducted to develop high-yielding rice lines with tolerance to drought and submergence by introgressing Sub1 into a rice line with drought yield QTL (qDTY; QTL = quantitative trait loci) viz. qDTY3.1 and qDTY12.1 using marker-assisted breeding. We report here the effect of different combinations of Sub1 and qDTY on morpho-physiological, agronomical traits and yield under reproductive stage drought stress (RS) and non-stress (NS) conditions. Lines with outstanding performance in RS and NS trials were also evaluated in vegetative stage submergence stress (VS) trial to assess the tolerance level. The QTL class analysis revealed Sub1 + qDTY3.1 as the best QTL combination affecting the measured traits in RS trial followed by Sub1 + qDTY12.1. The effects of single Sub1, qDTY3.1 and qDTY12.1 were not as superior as when the QTLs are combined, suggesting the positive interaction of Sub1 and qDTY. Best performing lines selected from the RS and NS trials recorded yield advantage up to 4453.69 kg ha-1 and 6954 kg ha-1 over the parents, respectively. The lines were also found having great tolerance to submergence ranging from 80% to 100%, contributed by a lower percentage of shoot elongation and reduction of chlorophyll content after 14 days of VS. These lines could provide yield sustainability to farmers in regions impacted with drought and submergence while serving as important genetic materials for future breeding programs.
Drought affects vegetation growth to a large extent. Understanding the dynamic changes of vegetation during drought is of great significance for agricultural and ecological management and climate change adaptation. The relations between vegetation and drought have been widely investigated, but how vegetation loss and restoration in response to drought remains unclear. Using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) data, this study developed an evaluation framework for exploring the responses of vegetation loss and recovery to meteorological drought, and applied it to the humid subtropical Pearl River basin (PRB) in southern China for estimating the loss and recovery of three vegetation types (forest, grassland, cropland) during drought using the observed NDVI changes. Results indicate that vegetation is more sensitive to drought in high-elevation areas (lag time 8 months). Vegetation loss (especially in cropland) is found to be more sensitive to drought duration than drought severity and peak. No obvious linear relationship between drought intensity and the extent of vegetation loss is found. Regardless of the intensity, drought can cause the largest probability of mild loss of vegetation, followed by moderate loss, and the least probability of severe loss. Large spatial variability in the probability of vegetation loss and recovery time is found over the study domain, with a higher probability (up to 50 %) of drought-induced vegetation loss and a longer recovery time (>7 months) mostly in the high-elevation areas. Further analysis suggests that forest shows higher but cropland shows lower drought resistance than other vegetation types, and grassland requires a shorter recovery time (4.2-month) after loss than forest (5.1-month) and cropland (4.8-month).
Dehydration-responsive element binding (DREB) transcription factor plays an important role in controlling the expression of abiotic stress responsive genes. An intronless oil palm EgDREB1 was isolated and confirmed to be a nuclear localized protein. Electrophoretic mobility shift and yeast one-hybrid assays validated its ability to interact with DRE/CRT motif. Its close evolutionary relation to the dicot NtDREB2 suggests a universal regulatory role. In order to determine its involvement in abiotic stress response, functional characterization was performed in oil palm seedlings subjected to different levels of drought severity and in EgDREB1 transgenic tomato seedlings treated by abiotic stresses. Its expression in roots and leaves was compared with several antioxidant genes using quantitative real-time PCR. Early accumulation of EgDREB1 in oil palm roots under mild drought suggests possible involvement in the initiation of signaling communication from root to shoot. Ectopic expression of EgDREB1 in T1 transgenic tomato seedlings enhanced expression of DRE/CRT and non-DRE/CRT containing genes, including tomato peroxidase (LePOD), ascorbate peroxidase (LeAPX), catalase (LeCAT), superoxide dismutase (LeSOD), glutathione reductase (LeGR), glutathione peroxidase (LeGP), heat shock protein 70 (LeHSP70), late embryogenesis abundant (LeLEA), metallothionine type 2 (LeMET2), delta 1-pyrroline-5- carboxylate synthetase (LePCS), ABA-aldehyde oxidase (LeAAO) and 9-cis- Epoxycarotenoid dioxygenase (LeECD) under PEG treatment and cold stress (4 °C). Altogether, these findings suggest that EgDREB1 is a functional regulator in enhancing tolerance to drought and cold stress.
Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.
Drought is a major abiotic stress that influences rice production. Although the transcriptomic data of rice against drought is widely available, the regulation of small open reading frames (sORFs) in response to drought stress in rice is yet to be investigated. Different levels of drought stress have different regulatory mechanisms in plants. In this study, drought stress was imposed on four-leaf stage rice, divided into two treatments, 40% and 30% soil moisture content (SMC). The RNAs of the samples were extracted, followed by the RNA sequencing analysis on their sORF expression changes under 40%_SMC and 30%_SMC, and lastly, the expression was validated through NanoString. A total of 122 and 143 sORFs were differentially expressed (DE) in 40%_SMC and 30%_SMC, respectively. In 40%_SMC, 69 sORFs out of 696 (9%) DEGs were found to be upregulated. On the other hand, 69 sORFs out of 449 DEGs (11%) were significantly downregulated. The trend seemed to be higher in 30%_SMC, where 112 (12%) sORFs were found to be upregulated from 928 significantly upregulated DEGs. However, only 8% (31 sORFs out of 385 DEGs) sORFs were downregulated in 30%_SMC. Among the identified sORFs, 110 sORFs with high similarity to rice proteome in the PsORF database were detected in 40%_SMC, while 126 were detected in 30%_SMC. The Gene Ontology (GO) enrichment analysis of DE sORFs revealed their involvement in defense-related biological processes, such as defense response, response to biotic stimulus, and cellular homeostasis, whereas enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways indicated that DE sORFs were associated with tryptophan and phenylalanine metabolisms. Several DE sORFs were identified, including the top five sORFs (OsisORF_3394, OsisORF_0050, OsisORF_3007, OsisORF_6407, and OsisORF_7805), which have yet to be characterised. Since these sORFs were responsive to drought stress, they might hold significant potential as targets for future climate-resilient rice development.
Boron is considered important to improve the drought resistance, yield and protein contents of pulses. Two years of field experiment was conducted to evaluate the effect of boron application and water stress given at vegetative and flowering stages on growth, yield and protein contents of mungbean during spring 2014 and 2015. The experiment was laid out in randomized complete block design with split-plot arrangement giving more emphasis to boron. The experiment comprised three water stress levels (normal irrigation, water stress at vegetative stage and water stress at reproductive phase) and four boron levels (0, 2, 4 and 6 kg ha-1). Final seed yield was significantly increased by different levels of boron application both under normal and water stressed conditions. The increase in yield was mainly due to greater plant height, number of pods bearing branches, number of pods per plant, number of seeds per pod and 1000-grain weight. Boron application at 4 kg ha-1 caused 17%, 10% and 4% increase in grain yield under normal irrigation, stress at vegetative stage and water stress at reproductive phase, respectively. Protein contents were also increased (9-16%) at same boron treatment. Most parameters showed a marked decrease at higher dose (6 kg ha-1) of boron. In conclusion, the boron application at rate of 4 kg ha-1 in clay-loam soil performed the best to enhance mungbean growth, yield and seed protein both under normal and water stressed conditions.
Elucidating the physiological mechanisms of the irregular yet concerted flowering rhythm of mass flowering tree species in the tropics requires long-term monitoring of flowering phenology, exogenous and endogenous environmental factors, as well as identifying interactions and dependencies among these factors. To investigate the proximate factors for floral initiation of mast seeding trees in the tropics, we monitored the expression dynamics of two key flowering genes, meteorological conditions and endogenous resources over two flowering events of Shorea curtisii and Shorea leprosula in the Malay Peninsula. Comparisons of expression dynamics of genes studied indicated functional conservation of FLOWERING LOCUS T (FT) and LEAFY (LFY) in Shorea. The genes were highly expressed at least 1 month before anthesis for both species. A mathematical model considering the synergistic effect of cool temperature and drought on activation of the flowering gene was successful in predicting the observed gene expression patterns. Requirement of both cool temperature and drought for floral transition suggested by the model implies that flowering phenologies of these species are sensitive to climate change. Our molecular phenology approach in the tropics sheds light on the conserved role of flowering genes in plants inhabiting different climate zones and can be widely applied to dissect the flowering processes in other plant species.
KEY MESSAGE: Morphological and transcriptomic evidences provide us strong support for the function of ANAC019 in reproductive development under drought stress. Plants are sensitive to drought conditions, particularly at the reproductive stage. Several studies have reported drought effects on crop reproductive development, but the molecular mechanism underlying drought response during reproduction is still unclear. A recent study showed that drought induces in Arabidopsis inflorescence increased expression of many genes, including ANAC019. However, the function of ANAC019 in drought response during reproductive development has not been characterized. Here, we report an investigation of the ANAC019 function in the response to drought during reproduction. ANAC019 is preferentially expressed in the inflorescence compared with the leaf, suggesting possible roles in regulating both stress response and flower development. The anac019 mutant was more sensitive to drought than WT plant, and exhibited a delay in recovery of floral organ development under prolonged drought stress. Moreover, many fewer genes were differentially expressed in the anac019 inflorescence under drought than that of WT, suggesting that the mutant was impaired in drought-induced gene expression. The genes affected by ANAC019 were associated with stress and hormone responses as well as floral development. In particular, the expression levels of several key drought-induced genes, DREB2A, DREB2B, ARF2, MYB21 and MYB24, were dramatically reduced in the absence of ANAC019, suggesting that ANAC019 is an upstream regulator these genes for drought response and flower development. These results provide strong support for the potential function of ANAC019 in reproductive development under drought stress.
Droughts are recurring events in Australia and cause a severe effect on agricultural and water resources. However, the studies about agricultural drought risk mapping are very limited in Australia. Therefore, a comprehensive agricultural drought risk assessment approach that incorporates all the risk components with their influencing criteria is essential to generate detailed drought risk information for operational drought management. A comprehensive agricultural drought risk assessment approach was prepared in this work incorporating all components of risk (hazard, vulnerability, exposure, and mitigation capacity) with their relevant criteria using geospatial techniques. The prepared approach is then applied to identify the spatial pattern of agricultural drought risk for Northern New South Wales region of Australia. A total of 16 relevant criteria under each risk component were considered, and fuzzy logic aided geospatial techniques were used to prepare vulnerability, exposure, hazard, and mitigation capacity indices. These indices were then incorporated to quantify agricultural drought risk comprehensively in the study area. The outputs depicted that about 19.2% and 41.7% areas are under very-high and moderate to high risk to agricultural droughts, respectively. The efficiency of the results is successfully evaluated using a drought inventory map. The generated spatial drought risk information produced by this study can assist relevant authorities in formulating proactive agricultural drought mitigation strategies.
Hyperdiverse tropical rainforests, such as the aseasonal forests in Southeast Asia, are supported by high annual rainfall. Its canopy is dominated by the species-rich tree family of Dipterocarpaceae (Asian dipterocarps), which has both ecological (e.g., supports flora and fauna) and economical (e.g., timber production) importance. Recent ecological studies suggested that rare irregular drought events may be an environmental stress and signal for the tropical trees. We assembled the genome of a widespread but near threatened dipterocarp, Shorea leprosula, and analyzed the transcriptome sequences of ten dipterocarp species representing seven genera. Comparative genomic and molecular dating analyses suggested a whole-genome duplication close to the Cretaceous-Paleogene extinction event followed by the diversification of major dipterocarp lineages (i.e. Dipterocarpoideae). Interestingly, the retained duplicated genes were enriched for genes upregulated by no-irrigation treatment. These findings provide molecular support for the relevance of drought for tropical trees despite the lack of an annual dry season.
Emmer wheat (Triticum dicoccon Schrank) is a potential source of new genetic diversity for the improvement of hexaploid bread wheat. Emmer wheat was crossed and backcrossed to bread wheat and 480 doubled haploids (DHs) were produced from BC1F1 plants with hexaploid appearance derived from 19 crossses. These DHs were screened under well-watered conditions (E1) in 2013 to identify high-yielding materials with similar phenology. One-hundred and eighty seven DH lines selected on this basis, 4 commercial bread wheat cultivars and 9 bread wheat parents were then evaluated in extensive field experiments under two contrasting moisture regimes in north-western NSW in 2014 and 2015. A significant range in the water-use-efficiency of grain production (WUEGrain) was observed among the emmer derivatives. Of these, 8 hexaploid lines developed from 8 different emmer wheat parents had significantly improved intrinsic water-use-efficiency (WUEintr) and instantaneous water-use-efficiency (WUEi) compared to their bread wheat recurrent parents. Accurate and large scale field-based phenotyping was effective in identifying emmer wheat derived lines with superior performance to their hexaploid bread wheat recurrent parents under moisture stress.
A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949-2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07-0.85, 0.08-0.76, 0.062-0.80 and 0.042-0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.