Active optical sensor measurements and weather variables for predicting winter wheat yield
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
Agronomy Journal
Abstract
Accurate winter wheat (Triticum aestivum L.) grain yield prediction is vital for
improving N management decisions. Currently, most N optimization algorithms use
in-season estimated yield (INSEY) as a sole variable for predicting grain yield potential
(YP). Although evidence suggests that this works, the yield prediction accuracy
could be further improved by including other predictors in the model. The objective
of this work was to evaluate INSEY, pre-plant N rate, total rainfall, and average air
temperature from September to December as predictors of winter wheat YP. An 8-
yr (2012–2019) data set for grain yield was obtained from Experiment 502, Lahoma,
OK. The experiment was designed as a randomized complete block with four replications
and N applied at 0, 45, 67, 90, and 112 kg ha–1.Weather data was obtained from
the OklahomaMesonet (http://mesonet.org). The data were analyzed using R statistical
computing platform. The best model was selected using least absolute shrinkage
and selection operator. Root mean square error (RMSE) was obtained using k-fold
cross-validation. The model selection algorithm produced the full model as the best
model for yield prediction with an R2 of .79 and RMSE of 0.54 Mg ha–1. The best
one-variable model – as expected – used INSEY as the predictor and had the highest
RMSE of 0.72 Mg ha–1 and an R2 of .62. Mid-season YP prediction accuracy could
be improved by including pre-plant N rate, mean air temperature, and total rainfall
from September to December in a model already containing INSEY.
Description
Keywords
Optical sensor measurements, Weather variables, Winter wheat yield
Citation
Aula, L., Omara, P., Nambi, E., Oyebiyi, F. B., Dhillon, J., Eickhoff, E., ... & Raun, W. R. (2021). Active optical sensor measurements and weather variables for predicting winter wheat yield. Agronomy Journal, 113(3), 2742-2751. DOI: 10.1002/agj2.20620