2012 Midwest Drought in the United States

Mallya, Ganeshchandra; Zhao, Lin; Song, Carol X.; Niyogi, Dev; Govindaraju, Rao S.

doi:10.1061/(asce)he.1943-5584.0000786

Cited by 132 publications

(85 citation statements)

References 9 publications

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“…The sensitivity of corn to drought stress is higher in silking stage, but lower in grain filling (blister to dent) and vegetative development (pre-silking), as discussed by Niyogi and Mishra [56]. As shown in the Figure 2, the drought development phase started in the reproduction stages, indicating the potential for significant impacts on the corn yield, as shown in the statistical data [2]. This highlights Previous studies from Hoerling et al [55] and Otkin et al [28] suggested the 2012 Great Plain drought belonged to a "flash drought", as it developed rapidly in May 2012 and had reached peak intensity by that August.…”

Section: Drought Evolution Processmentioning

confidence: 62%

“…A statistical estimate of crop yield loss caused by the 2012 US drought was demonstrated in Mallya et al [2], but it has a limited value when gauging ongoing drought severity. In order to aid this process of linking crop loss to severity, we analyzed the performance of the different drought indices in indicating the final yield loss at county-scale.…”

Section: Drought Severity Assessment From Yield Lossmentioning

confidence: 99%

“…The 2012 US drought is one example of an extreme, widespread drought disaster which led to serious crop losses throughout one of the largest agricultural regions in the country [2]. In recent decades, studies have focused on a range of drought related topics, including basic concepts [3], monitoring [4], impacts [5], vulnerability [6], and mitigation [7].…”

Section: Introductionmentioning

confidence: 99%

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Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

et al. 2017

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Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet been tested in United States. In this study, we quantified the severity of 2012 drought which affected the agricultural output for much of the Midwestern US. We used several popular drought indices, including the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index with multiple time scales, Palmer Drought Severity Index, Palmer Z-index, VegDRI, and PADI by comparing the spatial distribution, temporal evolution, and crop impacts produced by each of these indices with the United States Drought Monitor. Results suggested this drought incubated around June 2011 and ended in May 2013. While different drought indices depicted drought severity variously. SPI outperformed SPEI and has decent correlation with yield loss especially at a 6 months scale and in the middle growth season, while VegDRI and PADI demonstrated the highest correlation especially in late growth season, indicating they are complementary and should be used together. These results are valuable for comparing and understanding the different performances of drought indices in the Midwestern US.

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Section: Drought Evolution Processmentioning

confidence: 62%

Section: Drought Severity Assessment From Yield Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

et al. 2017

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“…The 2012 calendar year was characterized by widespread intense drought and anomalously high temperatures over a wide fraction of the central US, including Indiana (Mallya et al, 2013). Conditions at MMSF during the vegetation season of 2012 were considerably drier than during 2013.…”

Section: Sampling Methodsmentioning

confidence: 99%

Forest canopy interactions with nucleation mode particles

2014

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Abstract. Ultrafine particle size distributions through a deciduous forest canopy indicate that nucleation mode particle concentrations decline with depth into the canopy, such that number concentrations at the bottom of the canopy are an average of 16 % lower than those at the top. However, growth rates of nucleation mode particles (diameters 6-30 nm) are invariant with height within the canopy, which implies that the semi-volatile gases contributing to their growth are comparatively well-mixed through the canopy. Growth rates of nucleation mode particles during a meteorological drought year (2012) were substantially lower than during a meteorologically normal year with high soil water potential (2013). This may reflect suppression of actual biogenic volatile organic compound (BVOC) emissions by drought and thus a reduction in the production of condensable products during the drought-affected vegetation season. This hypothesis is supported by evidence that growth rates during the normal year exhibit a positive correlation with emissions of BVOC modeled on observed forest composition, leaf area index, temperature and photosynthetically active radiation (PAR), but particle growth rates during the drought-affected vegetation season are not correlated with modeled BVOC emissions. These data thus provide indirect evidence that drought stress in forests may reduce BVOC emissions and limit growth of nucleation mode particles to climate-relevant sizes.

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“…The 2012 drought was attributed to natural variations in weather patterns, which caused insufficient warm-season rains for the region (Hoerling et al, 2014). A lack of precipitation in concert with high temperatures led to an excessive soil moisture deficit (Porter and Semenov, 2005), causing portions of Indiana to reach the exceptional drought condition category (Heim, 2012;Mallya et al, 2013). The two soil-landscape classification types (SSURGO and ERU) were assessed using the R v metric that represents a reduction of within-map-unit variability in corn yield data.…”

Section: Results and Discussion Yield And Yield Variance Reductionmentioning

confidence: 99%

Validating a Digital Soil Map with Corn Yield Data for Precision Agriculture Decision Support

et al. 2016

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Capturing the variability in soil-landscape properties is a challenge for grain producers attempting to integrate spatial information into the decision process of precision agriculture (PA). Digital soil maps (DSMs) use traditional soil survey information and can be the basis for PA subfi eld delineation (e.g., management zones). However, public soil survey maps provide only general descriptions of soil-landscape features. Th erefore, improved DSMs are needed that use high-resolution data that more precisely model soil-landscape characteristics. Additionally, reliable methods are needed to validate DSM products for PA. Th e objective of this study was to validate with corn (Zea mays L.) yield data the performance of a new DSM product, termed Environmental Response Unit (ERU), compared with the USDA Soil Survey Geographic (SSURGO) soil map. Th e ERU was developed by integrating SSURGO information with highresolution elevation data. For validation, corn yield maps were collected and corrected for common data collection errors from 409 fi elds across Indiana, Iowa, Minnesota, and Nebraska in 2010 to 2012. Reductions in the area-weighted variance (R v ) of corn yield for ERU and SSURGO were calculated relative to the whole-fi eld variance. Th e average R v across all site-years for SSURGO and ERU was 16 and 25%, respectively, which equated to a 57% higher median yield variance reduction with ERU over SSURGO. Th is variance reduction technique showed the potential of ERU as an improved model better representing soillandscape properties that impact corn yield. Th is research also has application potential for determining the success of a DSM for identifying management zones in PA.

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2012 Midwest Drought in the United States

Cited by 132 publications

References 9 publications

Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

Forest canopy interactions with nucleation mode particles

Validating a Digital Soil Map with Corn Yield Data for Precision Agriculture Decision Support

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