DOI: 10.5061/dryad.ngf1vhj1r
title: Early-season biomass and weather enable robust cereal rye cover crop biomass predictions

description: Farmers need accurate estimates of winter cover crop biomass to make
 informed decisions on termination timing or to estimate potential release
 of nitrogen from cover crop residues to subsequent cash crops. Utilizing
 data from an extensive experiment across 11 states from 2016 to 2020, this
 study explores the most reliable predictors for determining cereal rye
 cover crop biomass at the time of termination. Our findings demonstrate a
 strong relationship between early-season and late-season cover crop
 biomass. Employing a random forest model, we predicted late-season cereal
 rye biomass with a margin of error of approximately 1,000 kg ha-1 based on
 early-season biomass, growing degree days, cereal rye planting and
 termination dates, photosynthetically active radiation, precipitation, and
 site coordinates as predictors. Our results suggest that similar modeling
 approaches could be combined with remotely sensed early-season biomass
 estimations to improve the accuracy of predicting winter cover crop
 biomass at termination for decision support tools.
description: <strong>2.1 Field sites and
 operations</strong>  
 Cereal rye cover crop biomass data used in the modeling
 approach were obtained from a field experiment conducted on research farms
 in 11 states between 2016 and 2020 (as outlined in Supplementary Table 1).
 Cereal rye was planted in 9.1 by 12.2 m plots in the late fall of each
 year, with four or five replicates per site-year. Management practices
 (i.e., cereal rye variety, seeding rates and methods) specific to each
 site were based on local norms. Biomass samples were collected from two
 0.5-m<sup>2</sup> quadrats in each plot at six weeks
 (hereafter referred to as “early-season biomass”) and two weeks
 (“late-season biomass”) prior to target dates for soybean planting. Cereal
 rye variety, as well as cereal rye planting and early and late-season
 biomass dates are summarized across sites and years in Supplementary Table
 2.   <strong>2.2 Data
 assembly and preparation</strong>
   In addition to early-season biomass,
 which was hypothesized to predict winter cover crop growth, weather
 variables related to temperature and radiation were used to model
 late-season biomass.  Minimum and maximum air temperatures (℃) and
 shortwave incoming solar radiation (W m<sup>-2</sup>) were
 extracted for each site-year on a daily basis  at a spatial resolution of
 0.125° by 0.125° from the North American Land Data Assimilation System
 Phase 2 dataset (Xia et al., 2012).  Cumulative growing degree days (CGDD)
 (-4.5° C base) were calculated over two time periods and negative values
 were omitted (Pessotto et al., 2023). “Early CGDD” and “early
 precipitation” were summed between cereal rye planting date and early
 termination date (six weeks prior to soybean planting), and “late CGDD”
 and “late precipitation” were summed between early termination and late
 termination date. Precipitation data were extracted from the
 multi-radar/multi-sensor system (<em>NOAA Multi-Radar/Multi-Sensor
 System (MRMS)</em>, 2023). Daily photosynthetically active radiation
 (PAR) was calculated from shortwave radiation using the
 <em>‘sw.to.par’</em> function in the
 <em>LakeMetabolizer</em> v.1.5.0 R package (Winslow et al.,
 2016). The mean of daily PAR was calculated for the period between early
 and late cover crop termination dates.
   <strong>2.3 Statistical
 Analyses</strong> <strong>2.3.1 Model
 selection to evaluate support for each
 covariate</strong>  
 All predictor variables, early-season cereal rye biomass,
 cereal rye planting date (Julian days), late termination date (Julian
 days), mean late PAR, and both early and late CGDD, were standardized by
 subtracting the mean and dividing by the standard deviation of each
 variable (Gelman, 2008).We examined all candidate predictor variables for
 collinearity using the vif function in the car package (v3.0-10) (Fox et
 al., 2018) and removed the precipitation variables because of their
 variance inflation factor scores &gt; 3 (Zuur et al., 2010). Site
 location (which varied occasionally from year to year within states) was
 input as a unique categorical variable for each set of field location
 coordinates.   We fit a
 generalized linear mixed effects model (GLMM) using the <em>glmer
 </em>function in the lme4 package (Bates et al., 2015) with a
 Gaussian error distribution and log link function due to overdispersion in
 the response variable (late-season cereal rye cover crop biomass in kg
 ha<sup>-1</sup>). We specified a hierarchical model with
 random intercepts for each location and for blocks (nested under each
 location) to address the non-independence of repeated measurements within
 the same locations and blocks through time (Pinheiro & Bates,
 2000). We fit a “global” model with all covariates that we hypothesized to
 be important including early-season cereal rye biomass, cereal rye
 planting date (in Julian days), late termination date, mean late PAR, and
 both early and late CGDD. We visually assessed model assumptions of
 homogeneity of variance across groups and normality of fitted
 residuals.  
 <strong>2.3.2 Random forest model and
 validation</strong>  
 To improve the accuracy of predictions, we also fit a random
 forest machine learning model on the dataset using the randomForest
 package v. 4.7–11 in R (Breiman, 2001; Liaw & Wiener, 2002). We
 specified a random forest model with the training parameters ntree set to
 1,000 and mtry set to 2 and included the same covariates as the GLMM,
 except we included site latitude and longitude coordinates separately
 rather than as categorical locations. Variable importance was calculated
 with the<em> randomForest </em>package; variables were ranked
 using %IncMSE, the mean decrease in prediction accuracy on the out of bag
 samples as each variable is randomly permuted.
   The dataset was randomly partitioned
 so that the random forest model was trained on 70% of the total data, and
 30% was withheld and used for model validation. We also used the same data
 partition to validate a version of the “global model” GLMM that was
 refitted to include only the training data. To assess how model
 performance varied across “low” and “high” cover crop biomass values, we
 evaluated it separately for “low” biomass observations of 4,000 kg ha-1 or
 less and “high” cereal rye biomass values greater than 4,000 kg
 ha<sup>-1</sup>. 
description: # Early-season biomass and weather enable robust cereal rye cover crop
 biomass predictions 
 Utilizing data from an extensive experiment across 11 states from 2016 to
 2020, this study explores the most reliable predictors for determining
 cereal rye cover crop biomass at the time of termination. The dataset
 includes experimental data such as early-season biomass, cereal rye
 planting and termination dates, and site coordinates, and we extracted
 weather data such as growing degree days, photosynthetically active
 radiation, and precipitation to predict cereal rye biomass at the time of
 termination. ## Description of the data and file structure Two data files
 are included; first, the "experimental_data.csv" which includes
 all of the data describing and collected as a result of the field
 experiments. Another file "experimental_and_weather_data.csv"
 joins the weather and experimental data. All columns are defined in detail
 in the "data_dictionary.csv" file. "NA" entries
 correspond to missing data. We include an R Project file and use the
 "here" package for data and directory organization. We intend
 for the data files to be contained in a "data" subdirectory in
 the root folder where the "CC_biomas_model.Rproj" file is
 located. ## Sharing/Access information This is a section for linking to
 other ways to access the data, and for linking to sources the data is
 derived from, if any. Links to other publicly accessible locations of the
 data that we used to derive weather variables: *
 *
 * ## Code/Software We include 3 R scripts developing using R v 4.3.1 and the following packages and versions: httr_1.4.7, lubridate_1.9.2, forcats_1.0.0, stringr_1.5.0, dplyr_1.1.3,\ purrr_1.0.2, readr_2.1.4, tidyr_1.3.0, tibble_3.2.1, ggplot2_3.4.4 , tidyverse_2.0.0, here_1.0.1, patchwork_1.1.3, GGally_2.2.0 merTools_0.6.1, arm_1.13-1, MASS_7.3-60 , sjPlot_2.8.15, effects_4.2-2, lmerTest_3.1-3, MuMIn_1.47.5, car_3.1-2, carData_3.0-5, lme4_1.1-34 Matrix_1.6-1.1, randomForest_4.7-1.1. The scripts are ordered in the intended order which they are designed to be run. "1_weather_data_for_CC_model.R" demonstrates how the weather data was downloaded and derived from the public sources mentioned above. "2_CC_biomass_modeling.R" explores covariation in the dataset and contains code to produce the GLMM model and some figures . "3_RF_model.R" contains the code to produce the random forest model and some figures.