The Purdue Agro-climatic (PAC) dataset for the U.S. Corn Belt: Development and initial results

Liu, Xing; Jacobs, Elin M.; Kumar, Anil; Biehl, L. L.; Andresen, Jeff; Niyogi, Dev

doi:10.1016/j.crm.2016.10.005

Cited by 7 publications

(6 citation statements)

References 44 publications

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“…These data are from individual 16-km 2 grids that correspond to the location of Ameriflux (http://ameriflux.lbl.gov/) and USDA-NRCS Soil Climate Analysis Network (SCAN; Schaefer, Cosh, & Jackson, 2007) observational sites. The production and evaluation of these simulated data are described in Liu et al (2016) and Niyogi et al (2017), including comparisons with empirical data from the corresponding Ameriflux and SCAN sites.…”

Section: Methodsmentioning

confidence: 99%

“…The production and evaluation of these simulated data are described in Liu et al. (2016) and Niyogi et al. (2017), including comparisons with empirical data from the corresponding Ameriflux and SCAN sites.…”

Section: Methodsmentioning

confidence: 99%

“…The production and evaluation of the simulated LIS data are described in Liu et al (2016) and Niyogi et al (2017). Data from the 10 individual grid-blocks used in this study are available via Purdue University Research Repository (PURR) and are listed in the reference section (Jacobs, 2019).…”

Section: Data Availabilitymentioning

confidence: 99%

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Drivers of regional soil water storage memory and persistence

Jacobs

Bertassello

Rao

2020

Vadose Zone Journal

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Soil water storage dynamics link landscape and climate processes with consequent impacts on hydrologic and biogeochemical cycles, and on ecosystem functions. We explore regional-scale, spatiotemporal dynamics of root-zone soil water storage using spatially explicit, long-term simulations from a land data assimilation system over an area of ∼2 million km 2 in the U.S. Midwest. We synthesize the dataset to examine spatial patterns of hydroclimatic forcing and soil and vegetation properties to identify the key drivers of soil water storage across the region. To identify differences in temporal hydrologic variability between sites with different soil and vegetation characteristics, we focus on daily data from 10 locations, representative of three major land cover types in the region. We use the concepts of (a) soil water memory to evaluate differences in landscape buffering of rainfall and (b) persistence to evaluate the threshold-crossing properties of statistically defined "wet" and "dry" soil water conditions. Power spectral analyses of soil water storage reveal that regionally consistent patterns emerge in memory at multiple temporal scales. Threshold-crossing analyses reveal corresponding similarity in persistence between sites. The analyses show that stochastic rainfall is the key driver of landscape hydrologic dynamics, with rainfall frequency as the primary determinant of persistence across the region, whereas differences in land cover and soil properties across the region have second-order influence. The synthesis approach we present here is useful for ecological and agricultural risk assessments of climate change impacts, including increasing frequency of extreme events such as droughts and floods, but primarily points to the need for better understanding of future precipitation changes.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Drivers of regional soil water storage memory and persistence

Jacobs

Bertassello

Rao

2020

Vadose Zone Journal

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“…The 4-km Parameter-elevation Relationships on Independent Slopes Mode (PRISM) [48] meteorological dataset, based on a state-of-the-art advanced interpolation of weather station data, is used to obtain the daily maximum temperature, daily minimum temperature and daily precipitation. The daily incoming solar radiation, a critical meteorological variable for crop growth models, is not available in the PRISM dataset and is in general difficult to obtain at high spatial resolution due to lack of measurements, although some early stage attempts have been made [55]. As a result, the 1-degree daily incoming solar radiation data from the National Aeronautics and Space Administration Prediction Of Worldwide Energy Resources (NASA POWER) [49] dataset is used and is linked to the county-years in MySQL by nearest-neighbor interpolation.…”

Section: Methodsmentioning

confidence: 99%

Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms

Levitan

Gross

2018

Remote Sensing

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Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growth model simulations and solar-reflective satellite measurements. Specifically, we showed that bidirectional long short-term memory networks (BLSTMs) can be trained to predict the in-season state variables and yields of Agricultural Production Systems sIMulator (APSIM) maize crop growth model simulations from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m satellite measurements over the United States Corn Belt at a regional scale. We evaluated the performance of the BLSTMs through both k-fold cross validation and comparison to regional scale ground-truth yields and phenology. Using k-fold cross validation, we showed that three distinct in-season maize state variables (leaf area index, aboveground biomass, and specific leaf area) can be retrieved with cross-validated R2 values ranging from 0.4 to 0.8 for significant portions of the season. Several other plant, soil, and phenological in-season state variables were also evaluated in the study for their retrievability via k-fold cross validation. In addition, by comparing to survey-based United State Department of Agriculture (USDA) ground truth data, we showed that the BLSTMs are able to predict actual county-level yields with R2 values between 0.45 and 0.6 and actual state-level phenological dates (emergence, silking, and maturity) with R2 values between 0.75 and 0.85. We believe that a potential application of this methodology is to develop satellite products to monitor in-season field-scale crop growth on a global scale by reproducing the methodology with field-scale crop growth model simulations (utilizing farmer-recorded field-scale agromanagement data) and collocated high-resolution satellite data (fused with moderate-resolution satellite data).

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“…Others have identified a Western Corn Belt (Grassini et al, 2011;Morell et al, 2016;Sahajpal et al, 2014;Wimberly et al, 2017;Wright and Wimberly, 2013) and an Eastern Corn Belt (Auch and Laingen, 2015;Kellner et al, 2016) in the Midwest. Many studies within the region make no attempt to define the Corn Belt (Angel et al, 2017;Golecha and Gan, 2016;Liu et al, 2016;McLaughlin and Reckhow, 2017;Turhollow et al, 2014). Thus, one may be left asking, "What is the Corn Belt, where is it currently, and how is it changing?…”

Section: Introductionmentioning

confidence: 99%

Where is the USA Corn Belt, and how is it changing?

Green

Kipka

David

et al. 2018

Science of The Total Environment

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The "Corn Belt" is a commonly used term, but often referenced as a vaguely defined region in the Midwest USA. A few key studies have delineated synoptic maps of the Corn Belt boundaries going back to the early 20th century, but a modern flexible and accessible framework for mapping the Corn Belt in space and time is needed. New tools provide reference maps for the Corn Belt in the 21st century and the ability to quantify space-time changes in corn cropping patterns. The Landuse and Agricultural Management Practices web-Service (LAMPS) was used to estimate the average corn (maize, Zea mays L.) area in each county of the contiguous 48 USA states for the years 2010-2016. LAMPS provides a modified areal Fraction of corn (F) used to map the Corn Belt at three intensity levels, for example. The resulting patterns illustrate a mostly contiguous Midwest Corn Belt surrounded by more scattered regions, including southern and eastern regions. We also mapped irrigated areas and temporal changes in F. Mapped patterns have the potential to help researchers study issues related to food, feed, biofuel, and water security.

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The Purdue Agro-climatic (PAC) dataset for the U.S. Corn Belt: Development and initial results

Cited by 7 publications

References 44 publications

Drivers of regional soil water storage memory and persistence

Drivers of regional soil water storage memory and persistence

Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms

Where is the USA Corn Belt, and how is it changing?

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