scholarly journals Extracting Agronomic Information from SMOS Vegetation Optical Depth in the US Corn Belt Using a Nonlinear Hierarchical Model

2020 ◽  
Vol 12 (5) ◽  
pp. 827
Author(s):  
Colin Lewis-Beck ◽  
Victoria A. Walker ◽  
Jarad Niemi ◽  
Petruţa Caragea ◽  
Brian K. Hornbuckle
Keyword(s):  
United States ◽  
Optical Depth ◽  
The United States ◽  
Development Stage ◽  
District Level ◽  
Corn Belt ◽  
Model Parameters ◽  
Growth Stages ◽  
Modeling Framework ◽  
Crop Development

Remote sensing observations that vary in response to plant growth and senescence can be used to monitor crop development within and across growing seasons. Identifying when crops reach specific growth stages can improve harvest yield prediction and quantify climate change. Using the Level 2 vegetation optical depth (VOD) product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) satellite, we retrospectively estimate the timing of a key crop development stage in the United States Corn Belt. We employ nonlinear curves nested within a hierarchical modeling framework to extract the timing of the third reproductive development stage of corn (R3) as well as other new agronomic signals from SMOS VOD. We compare our estimates of the timing of R3 to United States Department of Agriculture (USDA) survey data for the years 2011, 2012, and 2013. We find that 87%, 70%, and 37%, respectively, of our model estimates of R3 timing agree with USDA district-level observations. We postulate that since the satellite estimates can be directly linked to a physiological state (the maximum amount of plant water, or water contained within plant tissue per ground area) it is more accurate than the USDA data which is based upon visual observations from roadways. Consequently, SMOS VOD could be used to replace, at a finer resolution than the district-level USDA reports, the R3 data that has not been reported by the USDA since 2013. We hypothesize the other model parameters contain new information about soil and crop management and crop productivity that are not routinely collected by any federal or state agency in the Corn Belt.

scholarly journals Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

2018 ◽  
Vol 10 (12) ◽  
pp. 2027 ◽  
Author(s):  
Itiya Aneece ◽  
Prasad Thenkabail
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
The United States ◽  
Google Earth ◽  
Growth Stages ◽  
Agricultural Crops ◽  
Spectral Library ◽  
Crop Types ◽  
Google Earth Engine

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

scholarly journals First Report of Okra yellow mosaic Mexico virus in Okra in the United States

Plant Disease ◽  
2010 ◽  
Vol 94 (7) ◽  
pp. 924-924 ◽  
Author(s):  
C. Hernandez-Zepeda ◽  
T. Isakeit ◽  
A. Scott ◽  
J. K. Brown
Keyword(s):  
United States ◽  
The United States ◽  
Full Length ◽  
Bipartite Begomoviruses ◽  
Economic Losses ◽  
Growth Stages ◽  
First Report ◽  
Total Dna ◽  
Hidalgo County ◽  
Whitefly Vector

During the okra growing season from August to November of 2009, symptoms reminiscent of geminivirus infection were observed on 75% of ‘Green Emerald’ Abelmoschus esculentus (L.) Moench, plants in a 0.2-km2 field in Hidalgo County, TX. Visible symptoms consisted of irregular yellow patches on leaves, distinctive yellow borders on leaf edges, and chlorosis of subsequently developing leaves. The whitefly vector of begomoviruses, Bemisia tabaci (Genn.), infested okra plants in the early growth stages during late July 2009. Total DNA was isolated from the leaves of three symptomatic okra plant samples (1) and used as the PCR template to amplify a 575-bp fragment of the coat protein gene (CP) using the universal begomovirus primers AV494 and AC1048 (2). PCR products of the expected size were cloned into the pGEM-T Easy (Promega, Madison, WI) and sequenced using the universal M13F and M13 R primers. ClustalV alignment indicated 99 to 100% shared nucleotide (nt) identity, and BLAST analysis revealed that the closest relative was Okra yellow mosaic Mexico virus - Tetekalitla (OkYMMV) (GenBank Accession No. EF591631) at 98%. To amplify the full-length DNA-A and a possible cognate DNA-B component, one plant that was positive by CP-PCR and DNA sequencing was selected for further analysis. Total DNA from this plant was used as template for a second detection method that consisted of rolling circle amplification (RCA) using the TempliPhi 100 Amplification System (GE Healthcare). RCA is a non-sequence-specific approach that permits amplification of circular DNA. The RCA products were linearized to release unit length ~2.6 kb DNA-A and DNA-B components using BamHI, and EcoRI, respectively. These products were cloned into pGEM3zf+ (Promega) and sequenced using M13F and M13 R primers and then by primer walking (>300 base overlap). Full-length DNA-A and DNA-B components were obtained, respectively, at 2,613 bp (GenBank Accession No. HM035059) and 2,594 bp (GenBank Accession No HM035060). Alignment of the DNA-A component using ClustalV (MegAlign, DNASTAR, Madison, WI) with begomoviral sequences available in GenBank indicated that it was 99% identical to OkYMMV DNA-A (GenBank Accession No. DQ022611). The closest relative to the DNA-B component (ClustalV) was Sida golden mosaic virus (SiGMV) (GenBank Accession No. AJ250731) at 73%. The nt identity of the 172-nt ‘common region’ present in the DNA-A and DNA-B components was 99%, and the iterons (predicted Rep binding motif) were identical for the two components, indicating that they are a cognate pair. The genome organization was typical of other New World bipartite begomoviruses. The economic losses due to infection by this virus could not be determined because an early freeze killed the plants. Hidalgo County is adjacent to Tamaulipas, Mexico, where ~50 km2 of okra are grown and the whitefly vector is also present. The identification of OkYMMV based on two independent detection methods, and the presence of begomovirus-like symptoms together with the whitefly vector, provide robust evidence for the association of OkYMMV-TX with diseased okra plants. To our knowledge, this is the first report of OkYMMV-TX infecting okra crops in Texas and in the continental United States. References: (1) J. J. Doyle and J. L. Doyle. Focus 12:13, 1990. (2) S. Wyatt and J. K. Brown. Phytopathology 86:1288, 1996.

scholarly journals First Report of Fusarium proliferatum Causing Root Rot on Soybean (Glycine max) in the United States

Plant Disease ◽  
2011 ◽  
Vol 95 (10) ◽  
pp. 1316-1316 ◽  
Author(s):  
M. M. Díaz Arias ◽  
G. P. Munkvold ◽  
L. F. Leandro
Keyword(s):  
United States ◽  
Root Rot ◽  
Morphological Characteristics ◽  
Dry Weight ◽  
The United States ◽  
Growth Stages ◽  
Species Identity ◽  
First Report ◽  
Soybean Roots ◽  
Soybean Diseases

Fusarium spp. are widespread soilborne pathogens that cause important soybean diseases such as damping-off, root rot, Fusarium wilt, and sudden death syndrome. At least 12 species of Fusarium, including F. proliferatum, have been associated with soybean roots, but their relative aggressiveness as root rot pathogens is not known and pathogenicity has not been established for all reported species (2). In collaboration with 12 Iowa State University extension specialists, soybean roots were arbitrarily sampled from three fields in each of 98 Iowa counties from 2007 to 2009. Ten plants were collected from each field at V2-V3 and R3-R4 growth stages (2). Typical symptoms of Fusarium root rot (2) were observed. Symptomatic and asymptomatic root pieces were superficially sterilized in 0.5% NaOCl for 2 min, rinsed three times in sterile distilled water, and placed onto a Fusarium selective medium. Fusarium colonies were transferred to carnation leaf agar (CLA) and potato dextrose agar and later identified to species based on cultural and morphological characteristics. Of 1,230 Fusarium isolates identified, 50 were recognized as F. proliferatum based on morphological characteristics (3). F. proliferatum isolates produced abundant, aerial, white mycelium and a violet-to-dark purple pigmentation characteristic of Fusarium section Liseola. On CLA, microconidia were abundant, single celled, oval, and in chains on monophialides and polyphialides (3). Species identity was confirmed for two isolates by sequencing of the elongation factor (EF1-α) gene using the ef1 and ef2 primers (1). Identities of the resulting sequences (~680 bp) were confirmed by BLAST analysis and the FUSARIUM-ID database. Analysis resulted in a 99% match for five accessions of F. proliferatum (e.g., FD01389 and FD01858). To complete Koch's postulates, four F. proliferatum isolates were tested for pathogenicity on soybean in a greenhouse. Soybean seeds of cv. AG2306 were planted in cones (150 ml) in autoclaved soil infested with each isolate; Fusarium inoculum was applied by mixing an infested cornmeal/sand mix with soil prior to planting (4). Noninoculated control plants were grown in autoclaved soil amended with a sterile cornmeal/sand mix. Soil temperature was maintained at 18 ± 1°C by placing cones in water baths. The experiment was a completely randomized design with five replicates (single plant in a cone) per isolate and was repeated three times. Root rot severity (visually scored on a percentage scale), shoot dry weight, and root dry weight were assessed at the V3 soybean growth stage. All F. proliferatum isolates tested were pathogenic. Plants inoculated with these isolates were significantly different from the control plants in root rot severity (P = 0.001) and shoot (P = 0.023) and root (P = 0.013) dry weight. Infected plants showed dark brown lesions in the root system as well as decay of the entire taproot. F. proliferatum was reisolated from symptomatic root tissue of infected plants but not from similar tissues of control plants. To our knowledge, this is the first report of F. proliferatum causing root rot on soybean in the United States. References: (1) D. M. Geiser et al. Eur. J. Plant Pathol. 110:473, 2004. (2) G. L. Hartman et al. Compendium of Soybean Diseases. 4th ed. The American Phytopathologic Society, St. Paul, MN, 1999. (3) J. F. Leslie and B. A. Summerell. The Fusarium Laboratory Manual. Blackwell Publishing, Oxford, UK, 2006. (4) G. P. Munkvold and J. K. O'Mara. Plant Dis. 86:143, 2002.

scholarly journals Cost-effectiveness of implementing objective diagnostic verification of asthma in the United States

2019 ◽  
Author(s):  
Mohsen Yaghoubi ◽  
Amin Adibi ◽  
Zafar Zafari ◽  
J Mark FitzGerald ◽  
Shawn D. Aaron ◽  
...  
Keyword(s):  
Quality Of Life ◽  
United States ◽  
Cost Effectiveness ◽  
The United States ◽  
Model Parameters ◽  
List Type ◽  
Asthma Diagnosis ◽  
Verification Algorithm ◽  
Input Parameters

AbstractBackgroundAsthma diagnosis in the community is often made without objective testing.ObjectiveThe aim of this study was to evaluate the cost-effectiveness of implementing a stepwise objective diagnostic verification algorithm among patients with community-diagnosed asthma in the United States (US).MethodsWe developed a probabilistic time-in-state cohort model that compared a stepwise asthma verification algorithm based on spirometry and methacholine challenge test against the current standard of care over 20 years. Model input parameters were informed from the literature and with original data analyses when required. The target population was US adults (≥15 y/o) with physician-diagnosed asthma. The final outcomes were costs (in 2018 $) and quality-adjusted life years (QALYs), discounted at 3% annually. Deterministic and probabilistic analyses were undertaken to examine the effect of alternative assumptions and uncertainty in model parameters on the results.ResultsIn a simulated cohort of 10,000 adults with diagnosed asthma, the stepwise algorithm resulted in the removal of diagnosis in 3,366. This was projected to be associated with savings of $36.26 million in direct costs and a gain of 4,049.28 QALYs over 20 years. Extrapolating these results to the US population indicated an undiscounted potential savings of $56.48 billion over 20 years. Results were robust against alternative assumptions and plausible changes in values of input parameters.ConclusionImplementation of a simple diagnostic testing algorithm to verify asthma diagnosis might result in substantial savings and improvement in patients’ quality of life.Key MessagesCompared with current standards of practice, the implementation of an asthma verification algorithm among US adults with diagnosed asthma can be associated with reduction in costs and gain in quality of life.There is substantial room for improving patient care and outcomes through promoting objective asthma diagnosis.Capsule summaryAsthma ‘overdiagnosis’ is common among US adults. An objective, stepwise verification algorithm for re-evaluation of asthma diagnosis can result in substantial savings in costs and improvements in quality of life.

scholarly journals Introductory Models of COVID-19 in the United States

2021 ◽  
Vol 2 (3) ◽  
pp. 74-98
Author(s):  
Peter Hugo Nelson
Keyword(s):  
United States ◽  
The United States ◽  
Transition Function ◽  
Model Parameters ◽  
Microsoft Excel ◽  
New Model ◽  
Social Distancing ◽  
Unlimited Growth ◽  
Least Squares Techniques

ABSTRACT Students develop and test simple kinetic models of the spread of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Microsoft Excel is used as the modeling platform because it is nonthreatening to students and it is widely available. Students develop finite difference models and implement them in the cells of preformatted spreadsheets following a guided inquiry pedagogy that introduces new model parameters in a scaffolded step-by-step manner. That approach allows students to investigate the implications of new model parameters in a systematic way. Students fit the resulting models to reported cases per day data for the United States using least squares techniques with Excel's Solver. Using their own spreadsheets, students discover for themselves that the initial exponential growth of COVID-19 can be explained by a simplified unlimited growth model and by the susceptible-infected-recovered (SIR) model. They also discover that the effects of social distancing can be modeled using a Gaussian transition function for the infection rate coefficient and that the summer surge was caused by prematurely relaxing social distancing and then reimposing stricter social distancing. Students then model the effect of vaccinations and validate the resulting susceptible-infected-recovered-vaccinated (SIRV) model by showing that it successfully predicts the reported cases per day data from Thanksgiving through the holiday period up to 14 February 2021. The same SIRV model is then extended and successfully fits the fourth peak up to 1 June 2021, caused by further relaxation of social distancing measures. Finally, students extend the model up to the present day (27 August 2021) and successfully account for the appearance of the delta variant of the SARS-CoV-2 virus. The fitted model also predicts that the delta variant peak will be comparatively short, and the cases per day data should begin to fall off in early September 2021, counter to current expectations. This case study makes an excellent capstone experience for students interested in scientific modeling.

scholarly journals Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt

2018 ◽  
Vol 110 (6) ◽  
pp. 2552-2565 ◽  
Author(s):  
G. M. Bean ◽  
N. R. Kitchen ◽  
J. J. Camberato ◽  
R. B. Ferguson ◽  
F. G. Fernandez ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
Corn Belt ◽  
Optical Reflectance

scholarly journals The Polarimetric Sensitivity of SMAP-Reflectometry Signals to Crop Growth in the U.S. Corn Belt

2020 ◽  
Vol 12 (6) ◽  
pp. 1007
Author(s):  
Nereida Rodriguez-Alvarez ◽  
Sidharth Misra ◽  
Mary Morris
Keyword(s):  
United States ◽  
Soil Moisture ◽  
The United States ◽  
Crop Growth ◽  
Corn Belt ◽  
Retrieval Algorithm ◽  
Agricultural Crop ◽  
Crop Height ◽  
Crop Type ◽  
The Impact

Crop growth is an important parameter to monitor in order to obtain accurate remotely sensed estimates of soil moisture, as well as assessments of crop health, productivity, and quality commonly used in the agricultural industry. The Soil Moisture Active Passive (SMAP) mission has been collecting Global Positioning System (GPS) signals as they reflect off the Earth’s surface since August 2015. The L-band dual-polarization reflection measurements enable studies of the evolution of geophysical parameters during seasonal transitions. In this paper, we examine the sensitivity of SMAP-reflectometry signals to agricultural crop growth related characteristics: crop type, vegetation water content (VWC), crop height, and vegetation opacity (VOP). The study presented here focuses on the United States “Corn Belt,” where an extensive area is planted every year with mostly corn, soybean, and wheat. We explore the potential to generate regularly an alternate source of crop growth information independent of the data currently used in the soil moisture (SM) products developed with the SMAP mission. Our analysis explores the variability of the polarimetric ratio (PR), computed from the peak signals at V- and H-polarization, during the United States Corn Belt crop growing season in 2017. The approach facilitates the understanding of the evolution of the observed surfaces from bare soil to peak growth and the maturation of the crops until harvesting. We investigate the impact of SM on PR for low roughness scenes with low variability and considering each crop type independently. We analyze the sensitivity of PR to the selected crop height, VWC, VOP, and Normalized Differential Vegetation Index (NDVI) reference datasets. Finally, we discuss a possible path towards a retrieval algorithm based on Global Navigation Satellite System-Reflectometry (GNSS-R) measurements that could be used in combination with passive SMAP soil moisture algorithms to correct simultaneously for the VWC and SM effects on the electromagnetic signals.

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