Abstract
The purpose of this study was to determine spatial and temporal precipitation variability, and the effect of this variability on yield and profitability. On-farm precipitation data is currently being measured by site-specific farmers. One potential use of the data is to provide inputs for corn yield modeling, which has been performed with neural networks and simulation. Profitability of measuring on-farm data depends on spatial precipitation variability and its effect on yield. Precipitation and air temperature from corn silking to dent stages, scale of yield data, and a technology factor were used to model corn yield in east central Indiana at farm (250 ha), county, and state scales, using backpropagation neural networks with five data coding schemes. The best net gave a root-mean-squared error of 11.9% overall (10.9% farm, 10.5% county, 7.9% state yield), with maximum-value interval coding. Four rain gauges on the same farm, spaced apart 1.02 to 3.04 km, gave a median absolute deviation of precipitation among gauges, by corn and soybean phenological phase, of 0.25 to 1.73 mm·day−1 (spatial variability). Median absolute deviation from a reference year was 0.17 to 3.40 mm·day−1 (temporal/year-to-year variability). Spatial variability was less than temporal variability, and frequently less than 1 mm. Three precipitation data sources—a National Weather Service (NWS) station on the same farm, the nearest non-urban NWS station, and a weighted mean of three nearest non-urban NWS stations (27–35 km distance)—were used to simulate corn yield by 1-ha grid cells with CERES-Maize. The percent absolute difference of simulated yield among the three sources (effect of spatial precipitation variability) was 15.8%. The percent absolute difference from long-term mean (temporal variability) was 21.5%, of the same order as spatial variability. A choice among nitrogen application strategies—variable-rate versus whole-field application, starter versus no starter—was made for the same farm, using probability of profit from simulated corn yield based on the three precipitation data sources. The most profitable strategy was not dependent on the data source. Using data from the nearest NWS station was more profitable than measuring precipitation on-site.
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Claims 11
Claim · model-written summary
The best neural net had an overall yield prediction error of 11.9%.
- Taxon
- corn
Zea mays subsp. mayse1 - Outcome
- root-mean-squared errore0
- Value
- 11.9%e0
- Qualifier
- maximum-value interval codinge0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The best net gave a root-mean-squared error of 11.9% overall (10.9% farm, 10.5% county, 7.9% state yield), with maximum-value interval coding”
t0“crop yield”
e1“by corn and soybean phenological phase”
Claim · model-written summary
Farm scale yield error was 10.9%.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- farme0
- Value
- 10.9%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“10.9% farm”
t0“crop yield”
e1“by corn and soybean phenological phase”
Claim · model-written summary
County scale yield error was 10.5%.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- countye0
- Value
- 10.5%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“10.5% county”
t0“crop yield”
e1“by corn and soybean phenological phase”
Claim · model-written summary
State scale yield error was 7.9%.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- state yielde0
- Value
- 7.9%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“7.9% state yield”
t0“crop yield”
e1“by corn and soybean phenological phase”
Claim · model-written summary
Spatial precipitation variability among farm gauges was 0.25 to 1.73 mm·day−1.
- Taxon
- cornZea mays subsp. mayse0
- Outcome
- median absolute deviation of precipitation among gaugese0
- Value
- 0.25 to 1.73 mm·day−1e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“median absolute deviation of precipitation among gauges, by corn and soybean phenological phase, of 0.25 to 1.73 mm·day−1 (spatial variability)”
Claim · model-written summary
Temporal precipitation variability was 0.17 to 3.40 mm·day−1.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- Median absolute deviation from a reference yeare0
- Value
- 0.17 to 3.40 mm·day−1e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Median absolute deviation from a reference year was 0.17 to 3.40 mm·day−1 (temporal/year-to-year variability)”
t0“crop yield”
e1“by corn and soybean phenological phase”
Claim · model-written summary
Spatial variability was below temporal variability and frequently below 1 mm.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- Spatial variabilitye0
- Direction
- less thane0
- Value
- 1 mme0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Spatial variability was less than temporal variability, and frequently less than 1 mm”
t0“crop yield”
e1“by corn and soybean phenological phase”
Claim · model-written summary
Simulated corn yield differed by 15.8% across precipitation sources.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- percent absolute difference of simulated yield among the three sourcese0
- Value
- 15.8%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The percent absolute difference of simulated yield among the three sources (effect of spatial precipitation variability) was 15.8%”
t0“crop yield”
e1“by corn and soybean phenological phase”
Claim · model-written summary
Simulated yield differed from the long term mean by 21.5%.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- percent absolute difference from long-term meane0
- Value
- 21.5%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The percent absolute difference from long-term mean (temporal variability) was 21.5%”
t0“crop yield”
e1“by corn and soybean phenological phase”
Claim · model-written summary
The most profitable nitrogen strategy did not depend on precipitation data source.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- most profitable strategye0
- Direction
- not dependente0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The most profitable strategy was not dependent on the data source”
t0“crop yield”
e1“by corn and soybean phenological phase”
Claim · model-written summary
Using the nearest NWS station was more profitable than measuring precipitation on site.
- Taxon
- cornZea mays subsp. mayse1
- Outcome
- profitablee0
- Direction
- more profitablee0
- Comparator
- measuring precipitation on-sitee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Using data from the nearest NWS station was more profitable than measuring precipitation on-site”
t0“crop yield”
e1“by corn and soybean phenological phase”