Abstract
A process-based terrestrial ecosystem model, Agro-IBIS, was used to simulate maize yield in a 13-state region of the U.S. Corn Belt from 1958 to 1994 across a 0.5° terrestrial grid. For validation, county-level census [U.S. Department of Agriculture (USDA)] data of yield were detrended to calculate annual yield residuals. Daily atmospheric inputs from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis were used in conjunction with the Climate Research Unit's (CRU's) monthly climate anomaly dataset at 0.5° resolution and a weather generator to drive the model at a 60-min time step. Multiple simulations were used to quantify model sensitivity to hybrid selection (defined by growing degree-day requirements), planting date, and soil type. The calibration of raw yields and model capability to replicate interannual variability were tested. The spatial patterns of simulated mean bias error (mbe) of raw yields were largely unresponsive to variations in soil texture, optimum hybrid choices, and planting dates typical for the region. Simulated 15-yr mean yields had a significant bias that was higher than observations, with an mbe of 0.97 Mg ha−1 and a root-mean-squared error (rmse) of 1.75 Mg ha−1. Simulations of interannual maize yield variability appeared to have a relatively weak response to changes in soil type and were more responsive to a planting date than a hybrid selection. The correlation (r2) between observed and simulated yield residuals within individual 0.5° grid cells ranged from 0.0 to 0.6, and increased as additional scenarios of land management decisions within 0.5° grid cells were taken into account. The correlations appeared to have a weak but significant relationship to a reported harvested area (r2 = 0.36). Model simulations produced a larger absolute magnitude of interannual variability than observations (positive and negative simulated yield residuals over the region averaged 18% and −21%, compared with 13% and −17% for observations), but spatial patterns were consistent. It was determined that the impact of irrigation on maize yield in the western Great Plains must be properly accounted for in future modeling scenarios to capture the increase (36%) in mean yields and significantly decreased interannual variability compared to rain-fed maize for future sensitivity studies.
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Claims 15
Claim · model-written summary
Simulated 15-year mean maize yields had a significant bias higher than observations, with a mean bias error of 0.97 Mg ha−1.
- Outcome
- biase1
- Direction
- highere1
- Value
- 0.97 Mg ha−1e1
- Comparator
- observationse1
- Setting
- 13-state region of the U.S. Corn Belte0
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e1“Simulated 15-yr mean yields had a significant bias that was higher than observations, with an mbe of 0.97 Mg ha−1 and a root-mean-squared error (rmse) of 1.75 Mg ha−1”
Claim · model-written summary
Simulated 15-year mean maize yields had a root-mean-squared error of 1.75 Mg ha−1.
- Outcome
- root-mean-squared error (rmse)e1
- Value
- 1.75 Mg ha−1e1
- Setting
- 13-state region of the U.S. Corn Belte0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e1“Simulated 15-yr mean yields had a significant bias that was higher than observations, with an mbe of 0.97 Mg ha−1 and a root-mean-squared error (rmse) of 1.75 Mg ha−1”
Claim · model-written summary
The spatial pattern of simulated mean bias error of raw yields was largely unresponsive to soil texture.
- Treatment
- soil texturee2
- Outcome
- spatial patterns of simulated mean bias error (mbe) of raw yieldse2
- Direction
- no_changeinferrede2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e2“The spatial patterns of simulated mean bias error (mbe) of raw yields were largely unresponsive to variations in soil texture, optimum hybrid choices, and planting dates typical for the region”
Claim · model-written summary
The spatial pattern of simulated mean bias error of raw yields was largely unresponsive to optimum hybrid choices.
- Treatment
- optimum hybrid choicese2
- Outcome
- spatial patterns of simulated mean bias error (mbe) of raw yieldse2
- Direction
- no_changeinferrede2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e2“The spatial patterns of simulated mean bias error (mbe) of raw yields were largely unresponsive to variations in soil texture, optimum hybrid choices, and planting dates typical for the region”
Claim · model-written summary
The spatial pattern of simulated mean bias error of raw yields was largely unresponsive to planting dates.
- Treatment
- planting datese2
- Outcome
- spatial patterns of simulated mean bias error (mbe) of raw yieldse2
- Direction
- no_changeinferrede2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e2“The spatial patterns of simulated mean bias error (mbe) of raw yields were largely unresponsive to variations in soil texture, optimum hybrid choices, and planting dates typical for the region”
Claim · model-written summary
Simulated interannual maize yield variability showed a relatively weak response to changes in soil type.
- Treatment
- changes in soil typee3
- Outcome
- interannual maize yield variabilitye3
- Qualifier
- relatively weak responsee3
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e3“Simulations of interannual maize yield variability appeared to have a relatively weak response to changes in soil type and were more responsive to a planting date than a hybrid selection”
Claim · model-written summary
Simulated interannual maize yield variability was more responsive to planting date than to hybrid selection.
- Treatment
- planting datee3
- Outcome
- interannual maize yield variabilitye3
- Direction
- more responsivee3
- Comparator
- hybrid selectione3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e3“Simulations of interannual maize yield variability appeared to have a relatively weak response to changes in soil type and were more responsive to a planting date than a hybrid selection”
Claim · model-written summary
The correlation between observed and simulated yield residuals within 0.5° grid cells ranged from 0.0 to 0.6.
- Outcome
- correlation (r2) between observed and simulated yield residualse4
- Value
- 0.0 to 0.6e4
- Qualifier
- within individual 0.5° grid cellse4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e4“The correlation (r2) between observed and simulated yield residuals within individual 0.5° grid cells ranged from 0.0 to 0.6, and increased as additional scenarios of land management decisions within 0.5° grid cells were taken into account”
Claim · model-written summary
The correlation between observed and simulated yield residuals increased as additional land management scenarios were included within grid cells.
- Treatment
- additional scenarios of land management decisionse4
- Outcome
- correlation (r2) between observed and simulated yield residualse4
- Qualifier
- increasede4
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e4“The correlation (r2) between observed and simulated yield residuals within individual 0.5° grid cells ranged from 0.0 to 0.6, and increased as additional scenarios of land management decisions within 0.5° grid cells were taken into account”
Claim · model-written summary
The correlations between observed and simulated yield residuals had a weak but significant relationship to reported harvested area (r2 = 0.36).
- Treatment
- reported harvested areae5
- Outcome
- correlationse5
- Value
- r2 = 0.36e5
- Qualifier
- weak but significante5
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e5“The correlations appeared to have a weak but significant relationship to a reported harvested area (r2 = 0.36)”
Claim · model-written summary
Simulated positive yield residuals averaged 18% over the region, compared with 13% for observations, so simulated interannual variability was larger than observed.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e6“Model simulations produced a larger absolute magnitude of interannual variability than observations (positive and negative simulated yield residuals over the region averaged 18% and −21%, compared with 13% and −17% for observations)”
Claim · model-written summary
Simulated negative yield residuals averaged −21% over the region, compared with −17% for observations, so simulated interannual variability was larger than observed.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e6“Model simulations produced a larger absolute magnitude of interannual variability than observations (positive and negative simulated yield residuals over the region averaged 18% and −21%, compared with 13% and −17% for observations)”
Claim · model-written summary
The impact of irrigation on maize yield in the western Great Plains must be accounted for in future modeling scenarios.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e7“the impact of irrigation on maize yield in the western Great Plains must be properly accounted for in future modeling scenarios to capture the increase (36%) in mean yields and significantly decreased interannual variability compared to rain-fed maize”
Claim · model-written summary
Irrigation increased mean maize yields by 36% compared with rain-fed maize in the western Great Plains.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e7“the impact of irrigation on maize yield in the western Great Plains must be properly accounted for in future modeling scenarios to capture the increase (36%) in mean yields and significantly decreased interannual variability compared to rain-fed maize”
Claim · model-written summary
Irrigation significantly decreased interannual maize yield variability compared with rain-fed maize in the western Great Plains.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“used to simulate maize yield in a 13-state region of the U.S. Corn Belt”
e7“the impact of irrigation on maize yield in the western Great Plains must be properly accounted for in future modeling scenarios to capture the increase (36%) in mean yields and significantly decreased interannual variability compared to rain-fed maize”
The same questions in other papers
irrigation · yield · Zea mays (maize)Positive and negative results
790 papers in the same direction as this paper · 209 in the opposite direction · 16 reporting no difference from an alternative
- negative
- no effect
- negative
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