Drought-induced stress represents a significant challenge to agricultural production, exerting adverse effects on both plant growth and overall productivity. Therefore, the exploration of innovative long-term approaches for addressing drought stress within agriculture constitutes a crucial objective, given its vital role in enhancing food security. This article explores the potential use of Trichoderma, a well-known genus of plant growth-promoting fungi, to enhance plant tolerance to drought stress. Trichoderma species have shown remarkable potential for enhancing plant growth, inducing systemic resistance, and ameliorating the adverse impacts of drought stress on plants through the modulation of morphological, physiological, biochemical, and molecular characteristics. In conclusion, the exploitation of Trichoderma's potential as a sustainable solution to enhance plant drought tolerance is a promising avenue for addressing the challenges posed by the changing climate. The manifold advantages of Trichoderma in promoting plant growth and alleviating the effects of drought stress underscore their pivotal role in fostering sustainable agricultural practices and enhancing food security.
This paper proposes A Hybrid Wavelet-Auto-Regressive Integrated Moving Average (W-ARIMA) model to explore the ability of the hybrid model over an ARIMA model. It combines two methods, a Discrete Wavelet Transform (DWT) and ARIMA model using the Standardized Precipitation Index (SPI) drought data for forecasting drought modeling development. SPI data from January 1954 to December 2008 used was divided into two - (80%/20% for training/testing respectively). The results were compared with the conventional ARIMA model with Mean Square Error (MSE) and Mean Average Error (MAE) as an error measure. The results of the proposed method achieved the best forecasting performance.
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.
Rice is the most important staple crop in Malaysia and is cultivated all over the country, including the state of Sabah. The uniqueness of rice cultivation in Sabah lies in the type of rice itself, deriving mainly from local or non-commercial cultivars but with distinctive characteristics including long grains, aromatic properties, and drought tolerance. However, despite having these important agricultural traits, information on the genetic diversity of Sabah rice remains limited. Hence, the purpose of this study was to determine the genetic polymorphisms of Sabah rice using random amplification of polymorphic DNA (RAPD) markers. A total of 101 alleles were profiled, from which 94% were identified as polymorphic. Phylogenetic analysis grouped the rice samples into three clusters, with two clusters classifying the ability of rice to grow under different planting conditions, suitable for growth irrigate and upland condition. The first cluster was dominated by cultivars that could survive in wet (irrigated) areas, while the other featured those that were found in dry (upland) areas. Furthermore, two alleles, OPA-05-B2 and OPA-01-B11, were found to be unique to cultivars within the upland cluster and were thus proposed to be involved in dry environmental adaptation. The results of the present study provide an insight into the genetic relationships and diversity of Sabah rice.
Climate change is a product of human actions. The extreme events such as flash floods, droughts, heat waves, earthquakes, volcano eruptions and tsunamis seen in the world today are the result of indiscriminate human intrusion into the environment. Vulnerable countries and populations are the most affected by these climatic events. This places a burden on the resources of these countries. The Kyoto Protocol is a milestone in environmental management and the impetus created by it must be maintained by carrying out the much needed research into appropriate mitigating measures that will alleviate the climate
change impact globally. A paradigm shift is needed in addressing the associated risks on human health to assess socioeconomic determinants and the related impacts on disease burden. Some wealthy nations emphasize economic benefits and downplay sustainability goals, health and equality. However the rising cost of energy is beginning to influence their outlook towards this issue. The implications on economics, human health and wellbeing are implicit. In order to strike a balance between disadvantaged and privileged nations, many
international agencies are spearheading various research agenda to improve adaptation programmes on effects of changing climatic conditions on health. Malaysia too has such programmes initiated under its 5-year development plans.
Termites perform key ecological functions in tropical ecosystems, are strongly affected by variation in rainfall, and respond negatively to habitat disturbance. However, it is not known how the projected increase in frequency and severity of droughts in tropical rainforests will alter termite communities and the maintenance of ecosystem processes. Using a large-scale termite suppression experiment, we found that termite activity and abundance increased during drought in a Bornean forest. This increase resulted in accelerated litter decomposition, elevated soil moisture, greater soil nutrient heterogeneity, and higher seedling survival rates during the extreme El Niño drought of 2015-2016. Our work shows how an invertebrate group enhances ecosystem resistance to drought, providing evidence that the dual stressors of climate change and anthropogenic shifts in biotic communities will have various negative consequences for the maintenance of rainforest ecosystems.
Conventional hydrogeologic framework models used to compute ocean island sustainable yields and aquifer storage neglect the complexity of the nearshore and offshore submarine environment. However, the onshore aquifer at the island of Hawai'i exhibits a notable volumetric discrepancy between high-elevation freshwater recharge and coastal discharge. In this study, we present a novel transport mechanism of freshwater moving from onshore to offshore through a multilayer formation of water-saturated layered basalts with interbedded low-permeability layers of ash/soil. Marine electromagnetic imaging reveals ∼35 km of laterally continuous resistive layers that extend to at least 4 km from west of Hawai'i's coastline, containing about 3.5 km3 of freshened water. We propose that this newly found transport mechanism of fresh groundwater may be the governing mechanism in other volcanic islands. In such a scenario, volcanic islands worldwide can use these renewable offshore reservoirs, considered more resilient to climate change-driven droughts, as new water resources.
General flowering (GF) is a unique phenomenon wherein, at irregular intervals, taxonomically diverse trees in Southeast Asian dipterocarp forests synchronize their reproduction at the community level. Triggers of GF, including drought and low minimum temperatures a few months previously has been limitedly observed across large regional scales due to lack of meteorological stations. Here, we aim to identify the climatic conditions that trigger large-scale GF in Peninsular Malaysia using satellite sensors, Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), to evaluate the climatic conditions of focal forests. We observed antecedent drought, low temperature and high photosynthetic radiation conditions before large-scale GF events, suggesting that large-scale GF events could be triggered by these factors. In contrast, we found higher-magnitude GF in forests where lower precipitation preceded large-scale GF events. GF magnitude was also negatively influenced by land surface temperature (LST) for a large-scale GF event. Therefore, we suggest that spatial extent of drought may be related to that of GF forests, and that the spatial pattern of LST may be related to that of GF occurrence. With significant new findings and other results that were consistent with previous research we clarified complicated environmental correlates with the GF phenomenon.
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.
Drought and pests are primary abiotic and biotic factors proposed as selective filters acting on species distributions along rainfall gradients in tropical forests and may contribute importantly to species distributional limits, performance, and diversity gradients. Recent research demonstrates linkages between species distributions along rainfall gradients and physiological drought tolerance; corresponding experimental examinations of the contribution of pest pressure to distributional limits and potential interactions between drought and herbivory are limited. This study aims to quantitate differential performance and herbivory as a function of species range limits across a climatic and floristic transition in Southeast Asia. Khao Chong Botanical Garden, Thailand and Pasoh Forest Reserve, Malaysia straddle the Kangar-Pattani Line. A reciprocal transplantation across a seasonality gradient was established using two groups of species ("widespread" taxa whose distributions include seasonally dry forests and "aseasonal" taxa whose distributions are limited to aseasonal forests). Growth, biomass allocation, survival, and herbivory were monitored for 19 months. Systematic differences in performance were a function of species distribution in relation to rainfall seasonality. In aseasonal Pasoh, aseasonal species had both greater growth and survivorship than widespread species. These differences were not a function of differential herbivory as widespread and aseasonal species experienced similar damage in the aseasonal forest. In seasonally dry Khao Chong, widespread species showed higher survivorship than aseasonal species, but these differences were only apparent during drought. We link this differential performance to physiological mechanisms as well as differential tolerance of biotic pressure during drought stress. Systematic decreases in seedling survival in aseasonal taxa during drought corresponded with previously documented physiological differences and may be exacerbated by herbivore damage. These results have important implications for tropical diversity and community composition in light of predicted increases in the frequency and severity of drought in hyperdiverse tropical forests.
Rice which belongs to the grass family is vulnerable to water stress. As water resources get limited, the productivity of rice is affected especially in granaries located at drought prone areas. It would be even worse in granaries located in drought prone areas such as KADA that receives the lowest rainfall in Malaysia. Spermine (SPM), a polyamine compound that is found ubiquitiosly in plants is involved in adaptation of biotic and abiotic stresses. The effect of SPM on growth,grain filling and yield of rice at three main granaries namely, IADA BLS, MADA and KADA representing unlimited water, limited water and water stress conditions respectively, were tested during the main season. Additinally, the growth enhancer was also tested during off season at KADA. Spermine increased plant height, number of tillers per hill and chlorophyll content in all three granaries. Application of SPM improved yield by 38, 29 and 20% in MADA, KADA and IADA BLS, respectively. Harvest index showed 2.6, 6 and 16% increases at IADA BLS, KADA and MADA, respectively in SPM treated plants as compared to untreated. Except for KADA which showed a reduction in yield at 2.54 tha-1, SPM improved yield at MADA, 7.21 tha-1 and IADA BLS, 9.13 tha-1 as compared to the average yield at these respective granaries. In the second trial, SPM increased the yield to 7.0 and 6.4 tha-1 during main and off seasons, respectively, indicating that it was significantly higher than control and the average yield reported by KADA. The yield of SPM treatments improved by 25 and 33% with an increment of farmer's income at main and off seasons, respectively. Stomatal width was significantly higher than control at 11.89 µm. In conclusion, irrespective of the tested granaries and rice variety, spermine mediated plots displayed increment in grain yield.
Bambara groundnut (Vigna subterranea (L.) Verdc.) is an underutilised legume crop, which has long been recognised as a protein-rich and drought-tolerant crop, used extensively in Sub-Saharan Africa. The aim of the study was to identify quantitative trait loci (QTL) involved in agronomic and drought-related traits using an expression marker-based genetic map based on major crop resources developed in soybean. The gene expression markers (GEMs) were generated at the (unmasked) probe-pair level after cross-hybridisation of bambara groundnut leaf RNA to the Affymetrix Soybean Genome GeneChip. A total of 753 markers grouped at an LOD (Logarithm of odds) of three, with 527 markers mapped into linkage groups. From this initial map, a spaced expression marker-based genetic map consisting of 13 linkage groups containing 218 GEMs, spanning 982.7 cM (centimorgan) of the bambara groundnut genome, was developed. Of the QTL detected, 46% were detected in both control and drought treatment populations, suggesting that they are the result of intrinsic trait differences between the parental lines used to construct the cross, with 31% detected in only one of the conditions. The present GEM map in bambara groundnut provides one technically feasible route for the translation of information and resources from major and model plant species to underutilised and resource-poor crops.
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.
A cross between IR64 (high-yielding but drought-susceptible) and Aday Sel (drought-tolerant) rice cultivars yielded a stable line with enhanced grain yield under drought screening field trials at International Rice Research Institute. The major effect qDTY4.1 drought tolerance and yield QTL was detected in the IR77298-14-1-2-10 Backcrossed Inbred Line (BIL) and its IR87705-7-15-B Near Isogenic Line (NIL) with 93.9% genetic similarity to IR64. Although rice yield is extremely susceptible to water stress at reproductive stage, currently, there is only one report on the detection of drought-responsive microRNAs in inflorescence tissue of a Japonica rice line. In this study, more drought-responsive microRNAs were identified in the inflorescence tissues of IR64, IR77298-14-1-2-10 and IR87705-7-15-B via next-generation sequencing. Among the 32 families of inflorescence-specific non-conserved microRNAs that were identified, 22 families were up-regulated in IR87705-7-15-B. Overall 9 conserved and 34 non-conserved microRNA families were found as drought-responsive in rice inflorescence with 5 conserved and 30 non-conserved families induced in the IR87705-7-15-B. The observation of more drought-responsive non-conserved microRNAs may imply their prominence over conserved microRNAs in drought response mechanisms of rice inflorescence. Gene Ontology annotation analysis on the target genes of drought-responsive microRNAs identified in IR87705-7-15-B revealed over-representation of biological processes including development, signalling and response to stimulus. Particularly, four inflorescence-specific microRNAs viz. osa-miR5485, osa-miR5487, osa-miR5492 and osa-miR5517, and two non-inflorescence specific microRNAs viz. osa-miR169d and osa-miR169f.2 target genes that are involved in flower or embryonic development. Among them, osa-miR169d, osa-miR5492 and osa-miR5517 are related to flowering time control. It is also worth mentioning that osa-miR2118 and osa-miR2275, which are implicated in the biosynthesis of rice inflorescence-specific small interfering RNAs, were induced in IR87705-7-15-B but repressed in IR77298-14-1-2-10. Further, gene search within qDTY4.1 QTL region had identified multiple copies of NBS-LRR resistance genes (potential target of osa-miR2118), subtilisins and genes implicated in stomatal movement, ABA metabolism and cuticular wax biosynthesis.
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
Ephemeral wetlands in arid regions are often degraded or destroyed through poor land-use practice long before they are ever studied or prioritized for conservation. Climate change will likely also have implications for these ecosystems given forecast changes in rainfall patterns in many arid environments. Here, we present a conceptual diagram showing typical and modified ephemeral wetlands in agricultural landscapes and how modification impacts on species diversity and composition.
Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R2) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks.
Droughts are slow-moving natural hazards that gradually spread over large areas and capable of extending to continental scales, leading to severe socio-economic damage. A key challenge is developing accurate drought forecast model and understanding a models' capability to examine different drought characteristics. Traditionally, forecasting techniques have used various time-series approaches and machine learning models. However, the use of deep learning methods have not been tested extensively despite its potential to improve our understanding of drought characteristics. The present study uses a deep learning approach, specifically the Long Short-Term Memory (LSTM) to predict a commonly used drought measure, the Standard Precipitation Evaporation Index (SPEI) at two different time scales (SPEI 1, SPEI 3). The model was compared with other common machine learning method, Random Forests, Artificial Neural Networks and applied over the New South Wales (NSW) region of Australia, using hydro-meteorological variables as predictors. The drought index and predictor data were collected from the Climatic Research Unit (CRU) dataset spanning from 1901 to 2018. We analysed the LSTM forecasted results in terms of several drought characteristics (drought intensity, drought category, or spatial variation) to better understand how drought forecasting was improved. Evaluation of the drought intensity forecasting capabilities of the model were based on three different statistical metrics, Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The model achieved R2 value of more than 0.99 for both SPEI 1 and SPEI 3 cases. The variation in drought category forecasted results were studied using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) approach. The analysis revealed an AUC value of 0.83 and 0.82 for SPEI 1 and SPEI 3 respectively. The spatial variation between observed and forecasted values were analysed for the summer months of 2016-2018. The findings from the study show an improvement relative to machine learning models for a lead time of 1 month in terms of different drought characteristics. The results from this work can be used for drought mitigation purposes and different models need to be tested to further enhance our capabilities.
Accurate prediction of any type of natural hazard is a challenging task. Of all the various hazards, drought prediction is challenging as it lacks a universal definition and is getting adverse with climate change impacting drought events both spatially and temporally. The problem becomes more complex as drought occurrence is dependent on a multitude of factors ranging from hydro-meteorological to climatic variables. A paradigm shift happened in this field when it was found that the inclusion of climatic variables in the data-driven prediction model improves the accuracy. However, this understanding has been primarily using statistical metrics used to measure the model accuracy. The present work tries to explore this finding using an explainable artificial intelligence (XAI) model. The explainable deep learning model development and comparative analysis were performed using known understandings drawn from physical-based models. The work also tries to explore how the model achieves specific results at different spatio-temporal intervals, enabling us to understand the local interactions among the predictors for different drought conditions and drought periods. The drought index used in the study is Standard Precipitation Index (SPI) at 12 month scales applied for five different regions in New South Wales, Australia, with the explainable algorithm being SHapley Additive exPlanations (SHAP). The conclusions drawn from SHAP plots depict the importance of climatic variables at a monthly scale and varying ranges of annual scale. We observe that the results obtained from SHAP align with the physical model interpretations, thus suggesting the need to add climatic variables as predictors in the prediction model.
Studies conducted on the various geometric properties of skeletons of water bodies have shown highly promising results. However, these studies were made under the assumption that water bodies were static objects and that they remained constant over time. Water bodies are actually dynamic objects; they go through significant spatio-temporal changes due to drought and flood. In this study, the characterization of skeletons of simulated drought and flood of water bodies was performed. It was observed that as the drought level increased from 1 to 9, the average length of the skeletons decreased due to reduction in the size of the water bodies and increase in the number of water bodies. As the drought level increased from 9 to 15, the average length of the skeletons increased further due to vanishing of small water bodies. Flood caused an increase in the average length of the skeletons due to merging of adjacent water bodies. Power law relationships were observed between the average length of the skeletons of the simulated drought/flood and the level of drought/flood. The scaling exponent of these power laws which was named as a fractal dimension, indicated the rate of change of the average length of the skeletons of simulated drought/flood of water bodies over varying levels of drought/flood. However, errors observed in the goodness of fit of the plots indicated that monofractals were not sufficient to characterise the skeletons of simulated drought and flood of water bodies. Multifractals and lacunarity analysis were required for more accurate characterisation.