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1. Is a drought a drought in grasslands? Productivity responses to different types of drought

   https://doi.org/10.1007/s00442-020-04793-8  

   Carroll, Charles J. ; Slette, Ingrid J. ; Griffin-Nolan, Robert J. ; Baur, Lauren E. ; Hoffman, Ava M. ; Denton, Elsie M. ; Gray, Jesse E. ; Post, Alison K. ; Johnston, Melissa K. ; Yu, Qiang ; et al ( January 2021 , Oecologia)

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2. Drought Impact on Desert Ecosystems, Drought Network precipitation manipulation experiment in desert grasslands

   https://doi.org/10.6073/pasta/5c2eb1c145a3babf399a0587d0189c2e  

   Ohlert, Timothy J ; Collins, Scott L ( January 2021 , Environmental Data Initiative)

   Climate change amplifies the global water cycle, making droughts more frequent and more severe. The hot deserts of the U.S. rely on the stability and frequency of water availability in order to sustain biological communities, making these ecosystems incredibly vulnerable to anticipated alterations in the water cycle. This project seeks to understand which biotic and abiotic variables are principle in determining desert ecosystem sensitivity to drought? To answer these questions, we have installed a drought manipulation that will simulate an extreme drought event by reducing annual precipitation by 66% in seven desert sites. Plant abundance data are collected annually to track changes in the plant community. Data collection began in Spring 2018. Treatments at three Sevilleta sites began in Fall 2018 after data collection in October 2018. Treatments started at four sites in Arizona and California in March and April of 2019 and spring pretreatment data collection. The treatments will last for four years. 

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3. Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators

   https://doi.org/10.3389/fdata.2021.750536  

   Hatami Bahman Beiglou, Pouyan ; Luo, Lifeng ; Tan, Pang-Ning ; Pei, Lisi ( October 2021 , Frontiers in Big Data)

   The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is built based on multiple physical indicators as well as reported observations from local contributors before human analysts combine the information and produce the drought map using their best judgement. Since human subjectivity is included in the production of the USDM maps, it is not an entirely clear quantitative procedure for other entities to reproduce the maps. In this study, we developed a framework to automatically generate the maps through a machine learning approach by predicting the drought categories across the domain of study. A persistence model served as the baseline model for comparison in the framework. Three machine learning algorithms, logistic regression, random forests, and support vector machines, with four different groups of input data, which formed an overall of 12 different configurations, were used for the prediction of drought categories. Finally, all the configurations were evaluated against the baseline model to select the best performing option. The results showed that our proposed framework could reproduce the drought maps to a near-perfect level with the support vector machines algorithm and the group 4 data. The rest of the findings of this study can be highlighted as: 1) employing the past week drought data as a predictor in the models played an important role in achieving high prediction scores, 2) the nonlinear models, random forest, and support vector machines had a better overall performance compared to the logistic regression models, and 3) with borrowing the neighboring grid cells information, we could compensate the lack of training data in the grid cells with insufficient historical USDM data particularly for extreme and exceptional drought conditions. 

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4. The Effect of Drought on Transcriptome and Hormonal Profiles in Barley Genotypes With Contrasting Drought Tolerance

   https://doi.org/10.3389/fpls.2020.618491  

   Harb, Amal ; Simpson, Craig ; Guo, Wenbin ; Govindan, Ganesan ; Kakani, Vijaya Gopal ; Sunkar, Ramanjulu ( December 2020 , Frontiers in Plant Science)

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   Like many cereal crops, barley is also negatively affected by drought stress. However, due to its simple genome as well as enhanced stress resilient nature compared to rice and wheat, barley has been considered as a model to decipher drought tolerance in cereals. In the present study, transcriptomic and hormonal profiles along with several biochemical features were compared between drought-tolerant (Otis) and drought-sensitive (Baronesse) barley genotypes subjected to drought to identify molecular and biochemical differences between the genotypes. The drought-induced decrease in the leaf relative water content, net photosynthesis, and biomass accumulation was relatively low in Otis compared to Baronesse. The hormonal profiles did not reveal significant differences for majority of the compounds other than the GA20 and the cis-zeatin-o-glucoside (c-ZOG), whose levels were greatly increased in Otis compared to Baronesse under drought. The major differences that emerged from the transcriptome analysis are; (1), the overall number of differentially expressed genes was relatively low in drought-tolerant Otis compared to drought-sensitive Baronesse; (2), a wax biosynthesis gene (CER1), and NAC transcription factors were specifically induced in Otis but not in Baronesse; (3), the degree of upregulation of betaine aldehyde dehydrogenase and a homeobox transcription factor (genes with proven roles in imparting drought tolerance), was greater in Otis compared to Baronesse; (4) the extent of downregulation of gene expression profiles for proteins of the reaction center photosystem II (PSII) (D1 and D2) was low in Otis compared to Baronesse; and, (5), alternative splicing (AS) was also found to differ between the genotypes under drought. Taken together, the overall transcriptional responses were low in drought-tolerant Otis but the genes that could confer drought tolerance were either specifically induced or greatly upregulated in the tolerant genotype and these differences could be important for drought tolerance in barley. 

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5. Probabilistic estimates of drought impacts on agricultural production: Drought Impacts on Agriculture

   https://doi.org/10.1002/2017GL073606  

   Madadgar, Shahrbanou ; AghaKouchak, Amir ; Farahmand, Alireza ; Davis, Steven J. ( August 2017 , Geophysical Research Letters)

   Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980–2012, including the Millennium Drought in Australia (2001–2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25–45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops. 

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