Abstract
Remote sensing is widely utilized in agriculture for estimating corn (Zea mays L.) grain yield (CGY). Few studies have determined if the Normalized Difference Vegetation Index (NDVI) and/or Soil Plant Analysis Development (SPAD) can estimate CGY in Florida. From April to August 2022, in Live Oak, Florida, a field-scale experiment was conducted in two sites with irrigated corn using a complete randomized block design with six nitrogen (N) rates and four replicates per site. This study aimed to estimate CGY using NDVI alone or in combination with SPAD, plant height (PH), and N rate. CGY response curve served as a comparison standard. Fifteen data subsets were selected, and stepwise selection multiple linear regression analysis was utilized to generate each reduced equation (Model). In addition, the relative significance of the predictor variables was evaluated. The strongest correlations with CGY were demonstrated by N rate (r = 0.93), PH103 (r = 0.91), NDVI39 (r = 0.81), and SPAD60 (r = 0.93). Models with multiple variables showed a better fit than single-variable models. Model 15 (variables until tasseling - 60 DAP) demonstrated comparable performance with 92.8% of variance explained and RMSE = 1,315.685 kg ha−1. Regardless of the model, the N rate has always contributed the most to CGY. Although Model 1 had the best overall performance, it may not be feasible for growers to utilize a model with multiple terms. Consequently, Model 15 could estimate CGY in Florida based on PH and NDVI at 60 and 32 DAP, respectively.
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Claims 7
Claim · model-written summary
N rate, plant height, NDVI and SPAD correlated with corn grain yield at r = 0.93, 0.91, 0.81 and 0.93, respectively.
- Taxon
- corn
Zea mays subsp. mayst0 - Treatment
- N ratee0
- Outcome
- correlations with CGYe0
- Value
- r = 0.93e0
- Setting
- Floridat0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The strongest correlations with CGY were demonstrated by N rate (r = 0.93), PH103 (r = 0.91), NDVI39 (r = 0.81), and SPAD60 (r = 0.93).”
t0“corn grain yield prediction in Florida”
Claim · model-written summary
Models with multiple variables fit corn grain yield better than single-variable models.
- Taxon
- cornZea mays subsp. mayst0
- Outcome
- fite0
- Direction
- bettere0
- Comparator
- single-variable modelse0
- Setting
- Floridat0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Models with multiple variables showed a better fit than single-variable models.”
t0“corn grain yield prediction in Florida”
Claim · model-written summary
Model 15 explained 92.8% of corn grain yield variance.
- Taxon
- cornZea mays subsp. mayst0
- Outcome
- variance explainede0
- Value
- 92.8%e0
- Qualifier
- variables until tasseling - 60 DAPe0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Model 15 (variables until tasseling - 60 DAP) demonstrated comparable performance with 92.8% of variance explained and RMSE = 1,315.685 kg ha−1.”
t0“corn grain yield prediction in Florida”
Claim · model-written summary
Model 15 had an RMSE of 1,315.685 kg ha−1.
- Taxon
- cornZea mays subsp. mayst0
- Outcome
- RMSEe0
- Value
- 1,315.685 kg ha−1e0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Model 15 (variables until tasseling - 60 DAP) demonstrated comparable performance with 92.8% of variance explained and RMSE = 1,315.685 kg ha−1.”
t0“corn grain yield prediction in Florida”
Claim · model-written summary
N rate contributed the most to corn grain yield in every model.
- Taxon
- cornZea mays subsp. mayst0
- Treatment
- N ratee0
- Outcome
- CGYe0
- Qualifier
- Regardless of the modele0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Regardless of the model, the N rate has always contributed the most to CGY.”
t0“corn grain yield prediction in Florida”
Claim · model-written summary
Model 1 had the best overall performance.
- Taxon
- cornZea mays subsp. mayst0
- Outcome
- overall performancee0
- Direction
- beste0
- Comparator
- Model 1e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Although Model 1 had the best overall performance, it may not be feasible for growers to utilize a model with multiple terms.”
t0“corn grain yield prediction in Florida”
Claim · model-written summary
Model 15 could estimate corn grain yield in Florida using height at 60 DAP and NDVI at 32 DAP.
- Taxon
- cornZea mays subsp. mayst0
- Outcome
- estimate CGYe0
- Value
- 60 and 32 DAPe0
- Setting
- Floridae0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Consequently, Model 15 could estimate CGY in Florida based on PH and NDVI at 60 and 32 DAP, respectively.”
t0“corn grain yield prediction in Florida”