Paper · 1995 · Agronomy Journal

Fractal Description of Temporal Yield Variability of 10 Crops in the United States

Bahman Eghball; James F. Power

10
claims
11
verbatim quotes
53
citations
2
research areas
—
Not open access

Abstract

Abstract Fractal analysis has been used to characterize both temporal and spatial variability in plant and soil parameters. A plant parameter of prime concern is crop yield. Consequently, the temporal variability of 10 crops commonly grown in the USA was described using fractal analysis. Average yields of nine grain crops along with fiber yield of cotton ( Gossypium hirsutum L.) from 1930 to 1990 in the USA were used for semivariogram and fractal analyses. Semivariance was calculated for each crop for different year intervals ( h ). The slope of the regression line of log semivariance vs. log h for each crop was used to calculate fractal dimension [ D = (4 — slope)/2], which is an indication of the pattern of yield variability. A small D ‐value (near 1) indicates the dominance of long‐term variation, while a large D ‐value (near 2) indicates dominance of short‐term variation and nondominance or lack of long‐term variation or trend. From 1930 to 1990, yield of all crops increased, ranging from about twofold in soybean [ Glycine max (L.) Merr.], oat ( Avena sativa L.), and barley ( Hordeum vulgare L.) to about sixfold in maize ( Zea mays L.). Crop improvement through plant breeding and the use of fertilizers and pesticides are presumably the main reasons for increased yield. Large yield differences were observed after 1960 for most of the crops studied, suggesting that risk resulting from year‐to‐year yield differences increased with improved yields. Fractal dimensions ranged from 1.20 to 1.47 for the crops studied, identifying long‐term trend as well as short‐term variation in yield of these crops. Rice ( Oryza sativa L.) had the smallest D ‐value, indicating that this crop had the least short‐term variation, while oat and soybean had the largest D , indicating greatest short‐term variation. Temporal variability in average crop yield in the USA was much smaller than typical spatial variability values reported by others for soil parameters. It appears that fractal analysis is useful in quantifying temporal variability in yield of various crops and can be applied to agronomic research to characterize temporal variations.

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Claims 10

Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

Soybean yield rose about twofold from 1930 to 1990.

Taxon
soybeanGlycine maxe0
Outcome
yielde0
Direction
increasede0
Value
about twofolde0
Setting
USAe4

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Glycine max
Glycine max · TaxonPublic domain · Wikimedia Commons ↗

Verbatim

e0

“From 1930 to 1990, yield of all crops increased, ranging from about twofold in soybean [ Glycine max (L.) Merr.], oat ( Avena sativa L.), and barley ( Hordeum vulgare L.) to about sixfold in maize ( Zea mays L.).”

✓ located verbatim in the source · abstract, characters 946–1158 · show in text ↑
e4

“Temporal variability in average crop yield in the USA was much smaller than typical spatial variability values reported by others for soil parameters.”

✓ located verbatim in the source · abstract, characters 1822–1972 · show in text ↑
Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

Oat yield rose about twofold.

Outcome
yielde0
Direction
increasede0
Value
about twofolde0

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Avena sativa
Avena sativa · TaxonSendelbach ( talk ) · CC BY-SA 3.0 · Wikimedia Commons ↗

Verbatim

e0

“From 1930 to 1990, yield of all crops increased, ranging from about twofold in soybean [ Glycine max (L.) Merr.], oat ( Avena sativa L.), and barley ( Hordeum vulgare L.) to about sixfold in maize ( Zea mays L.).”

✓ located verbatim in the source · abstract, characters 946–1158 · show in text ↑
Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

Barley yield rose about twofold.

Outcome
yielde0
Direction
increasede0
Value
about twofolde0

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Hordeum vulgare
Hordeum vulgare · TaxonJackyM59 · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“From 1930 to 1990, yield of all crops increased, ranging from about twofold in soybean [ Glycine max (L.) Merr.], oat ( Avena sativa L.), and barley ( Hordeum vulgare L.) to about sixfold in maize ( Zea mays L.).”

✓ located verbatim in the source · abstract, characters 946–1158 · show in text ↑
Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

Maize yield rose about sixfold.

Taxon
maizeZea mayse0
Outcome
yielde0
Direction
increasede0
Value
about sixfolde0

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Zea mays
Zea mays · TaxonAkwugo · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“From 1930 to 1990, yield of all crops increased, ranging from about twofold in soybean [ Glycine max (L.) Merr.], oat ( Avena sativa L.), and barley ( Hordeum vulgare L.) to about sixfold in maize ( Zea mays L.).”

✓ located verbatim in the source · abstract, characters 946–1158 · show in text ↑
Observationobservedoriginal resultmeasured

Claim · model-written summary

Most crops had large yield differences after 1960.

Taxon
cropse1
Outcome
yield differencese1
Direction
Largee1

Verbatim

e1

“Large yield differences were observed after 1960 for most of the crops studied”

✓ located verbatim in the source · abstract, characters 1294–1372 · show in text ↑
Observationobservedoriginal resultmeasured

Claim · model-written summary

Yield fractal dimensions ranged from 1.20 to 1.47.

Taxon
cropse2
Outcome
Fractal dimensionse2
Value
1.20 to 1.47e2

Verbatim

e2

“Fractal dimensions ranged from 1.20 to 1.47 for the crops studied, identifying long‐term trend as well as short‐term variation in yield of these crops.”

✓ located verbatim in the source · abstract, characters 1473–1624 · show in text ↑
Observationobservedoriginal resultmeasured

Claim · model-written summary

Rice had the least short-term yield variation.

Outcome
short‐term variatione3
Direction
leaste3

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Oryza sativa
Oryza sativa · Taxon · picture from its Wikimedia Commons categoryDesmanthus4food · CC BY-SA 3.0 us · Wikimedia Commons ↗

Verbatim

e3

“Rice ( Oryza sativa L.) had the smallest D ‐value, indicating that this crop had the least short‐term variation, while oat and soybean had the largest D , indicating greatest short‐term variation.”

✓ located verbatim in the source · abstract, characters 1625–1821 · show in text ↑
Observationobservedoriginal resultmeasured

Claim · model-written summary

Oat and soybean had the greatest short-term yield variation.

Taxon
oat and soybeanGlycine maxAvena sativae3
Outcome
short‐term variatione3
Direction
greateste3

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Glycine max
Glycine max · TaxonPublic domain · Wikimedia Commons ↗
Avena sativa
Avena sativa · TaxonSendelbach ( talk ) · CC BY-SA 3.0 · Wikimedia Commons ↗

Verbatim

e3

“Rice ( Oryza sativa L.) had the smallest D ‐value, indicating that this crop had the least short‐term variation, while oat and soybean had the largest D , indicating greatest short‐term variation.”

✓ located verbatim in the source · abstract, characters 1625–1821 · show in text ↑
Observationobservedoriginal resultmeasured

Claim · model-written summary

Average crop yield varied less over time than soil parameters varied spatially.

Taxon
crope4
Outcome
Temporal variability in average crop yielde4
Direction
much smallere4
Comparator
typical spatial variability values reported by others for soil parameterse4
Setting
USAe4

Verbatim

e4

“Temporal variability in average crop yield in the USA was much smaller than typical spatial variability values reported by others for soil parameters.”

✓ located verbatim in the source · abstract, characters 1822–1972 · show in text ↑
Method / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

Fractal analysis was judged useful for quantifying temporal yield variation.

Taxon
cropse5
Outcome
quantifying temporal variability in yielde5
Qualifier
usefule5

Not reportedComparatorValue

Verbatim

e5

“It appears that fractal analysis is useful in quantifying temporal variability in yield of various crops”

✓ located verbatim in the source · abstract, characters 1973–2077 · show in text ↑