Paper · 2020 · Journal of Agricultural Science

Irrigation Scheduling to Promote Corn Productivity in Central Alabama

Jose F. Da Cunha Leme Filho; Brenda V. Ortiz; Damianos Damianidis; et al.

8
claims
18
verbatim quotes
8
citations
1
research areas
✓
Open access · cc-by

Abstract

Agriculture is the largest consumer of water in the United States. Results from previous studies have shown that it is possible to substantially reduce irrigation amounts and maintain corn yield. The objectives of this study were to evaluate the advantages and disadvantages of two irrigation scheduling methods for corn production in Alabama. Two irrigation scheduling methods evaluated were: a) Checkbook, which is one of the conventional methods used by farmers that is based on the soil water balance estimated using water lost by evapotranspiration and its replacement through rainfall or irrigation, and b) Sensor-based, which was based on soil matric potential values recorded by soil moisture tension sensors installed in the field. The experimental field was divided into two irrigation management zones (zone A and zone B) based on soil properties of each field. During the 2014 season in zone A, significant grain yield differences were observed between the two irrigation methods. The Checkbook plots exhibited greater yield than Sensor-based plots: 10181 kg ha-1 and 9696 kg ha-1, respectively. The greater yield on the Checkbook plots could be associated with higher irrigation rate applied, 148 mm more, compared with the Sensor-based plots. In zone B, there were no significant yield differences between both irrigation methods; however, Sensor-based plots out yielded Checkbook plots, with 9673 kg ha-1 and 9584 kg ha-1, respectively. Even though the irrigation amount applied in Checkbook located in zone B was higher, 102 mm more, there were no significant yield differences. Therefore, it suggests that the Sensor-based method was promissory irrigation scheduling strategy under the conditions of zone B. In 2015, there were no significant grain yield differences between zone A and zone B when the data from the Checkbook plots were analyzed. However, the Sensor-based treatment produced a statistically significant difference of grain yield of 13597 kg ha-1 in zone A and 11659 kg ha-1 in zone B, also both zones received the same amount of irrigation. Overall results of both growing seasons indicated that the use of the Sensor-based irrigation scheduling treatment resulted in similar values of total profit per hectare when compared to Checkbook method. The Sensor-based method seems a promising strategy that could result in water and financial savings, but more research is required.

Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗

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Claims 8

Intervention effectobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

In 2014 zone A, Checkbook grain yield exceeded Sensor-based yield significantly.

Treatment
Checkbooke0
Outcome
grain yielde0
Direction
greatere0
Value
10181 kg ha-1e0
Qualifier
9696 kg ha-1e0
Comparator
Sensor-basede0
Setting
zone Ae0

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

Zea mays subsp. mays
Zea mays subsp. mays · TaxonDominicus Johannes Bergsma · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“During the 2014 season in zone A, significant grain yield differences were observed between the two irrigation methods. The Checkbook plots exhibited greater yield than Sensor-based plots: 10181 kg ha-1 and 9696 kg ha-1, respectively”

✓ located verbatim in the source · abstract, characters 873–1106 · show in text ↑
t0

“Corn”

✓ located verbatim in the source · title, characters 33–37 · show in text ↑
Intervention effectobservedoriginal resultmeasured

Claim · model-written summary

Checkbook irrigation in zone A applied 148 mm more than Sensor-based irrigation.

Treatment
Checkbooke1
Outcome
irrigation ratee0
Direction
highere0
Value
148 mme0
Comparator
Sensor-basede0

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

Zea mays subsp. mays
Zea mays subsp. mays · TaxonDominicus Johannes Bergsma · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“higher irrigation rate applied, 148 mm more, compared with the Sensor-based plots”

✓ located verbatim in the source · abstract, characters 1174–1255 · show in text ↑
e1

“The Checkbook plots exhibited greater yield”

✓ located verbatim in the source · abstract, characters 993–1036 · show in text ↑
t0

“Corn”

✓ located verbatim in the source · title, characters 33–37 · show in text ↑
Intervention effectobservedoriginal resultnot significant (as stated)measured

Claim · model-written summary

In 2014 zone B, Sensor-based yield exceeded Checkbook yield without a significant difference.

Treatment
Sensor-basede0
Outcome
yielde0
Direction
out yieldede0
Value
9673 kg ha-1e0
Qualifier
9584 kg ha-1e0
Comparator
Checkbooke0
Setting
zone Be0

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

Zea mays subsp. mays
Zea mays subsp. mays · TaxonDominicus Johannes Bergsma · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“In zone B, there were no significant yield differences between both irrigation methods; however, Sensor-based plots out yielded Checkbook plots, with 9673 kg ha-1 and 9584 kg ha-1, respectively”

✓ located verbatim in the source · abstract, characters 1257–1450 · show in text ↑
t0

“Corn”

✓ located verbatim in the source · title, characters 33–37 · show in text ↑
Intervention effectobservedoriginal resultmeasured

Claim · model-written summary

Checkbook irrigation in zone B applied 102 mm more irrigation.

Treatment
Checkbooke0
Outcome
irrigation amounte0
Direction
highere0
Value
102 mme0
Comparator
Sensor-basede1
Setting
zone Be0

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

Zea mays subsp. mays
Zea mays subsp. mays · TaxonDominicus Johannes Bergsma · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“irrigation amount applied in Checkbook located in zone B was higher, 102 mm more”

✓ located verbatim in the source · abstract, characters 1468–1548 · show in text ↑
e1

“Sensor-based plots out yielded Checkbook plots”

✓ located verbatim in the source · abstract, characters 1354–1400 · show in text ↑
t0

“Corn”

✓ located verbatim in the source · title, characters 33–37 · show in text ↑
Observationobservedoriginal resultno effectnot significant (as stated)measured

Claim · model-written summary

In 2015, Checkbook grain yield did not differ significantly between zones A and B.

Treatment
Checkbooke0
Outcome
grain yielde0
Direction
no significante0
Comparator
zone Be0
Setting
zone Ae0

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

Zea mays subsp. mays
Zea mays subsp. mays · TaxonDominicus Johannes Bergsma · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“In 2015, there were no significant grain yield differences between zone A and zone B when the data from the Checkbook plots were analyzed”

✓ located verbatim in the source · abstract, characters 1725–1862 · show in text ↑
t0

“Corn”

✓ located verbatim in the source · title, characters 33–37 · show in text ↑
Observationobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

In 2015, Sensor-based yield in zone A significantly exceeded zone B.

Outcome
grain yielde0
Value
13597 kg ha-1e0
Qualifier
11659 kg ha-1e0
Comparator
zone Be0
Setting
zone Ae0

Not reportedDirection

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

Zea mays subsp. mays
Zea mays subsp. mays · TaxonDominicus Johannes Bergsma · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“Sensor-based treatment produced a statistically significant difference of grain yield of 13597 kg ha-1 in zone A and 11659 kg ha-1 in zone B”

✓ located verbatim in the source · abstract, characters 1877–2017 · show in text ↑
t0

“Corn”

✓ located verbatim in the source · title, characters 33–37 · show in text ↑
Intervention effectobservedoriginal resultmeasured

Claim · model-written summary

Sensor-based and Checkbook irrigation produced similar total profit per hectare across seasons.

Treatment
Sensor-based irrigation scheduling treatmente0
Outcome
total profit per hectaree0
Direction
similare0
Comparator
Checkbook methode0

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

Zea mays subsp. mays
Zea mays subsp. mays · TaxonDominicus Johannes Bergsma · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“use of the Sensor-based irrigation scheduling treatment resulted in similar values of total profit per hectare when compared to Checkbook method”

✓ located verbatim in the source · abstract, characters 2134–2278 · show in text ↑
t0

“Corn”

✓ located verbatim in the source · title, characters 33–37 · show in text ↑
Recommendationauthor interpretationoriginal resultpositivebasis n/a

Claim · model-written summary

The authors consider Sensor-based scheduling promising for water and financial savings, pending further research.

Treatment
Sensor-based methode0
Outcome
water and financial savingse0
Qualifier
more research is requirede0

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

Zea mays subsp. mays
Zea mays subsp. mays · TaxonDominicus Johannes Bergsma · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e0

“The Sensor-based method seems a promising strategy that could result in water and financial savings, but more research is required”

✓ located verbatim in the source · abstract, characters 2280–2410 · show in text ↑
t0

“Corn”

✓ located verbatim in the source · title, characters 33–37 · show in text ↑