Wajdi Belgacem
Selected projects

Climate risk · Conservation adoption

Climate Risk & Conservation Adoption

Analyzing the relationship between extreme weather and established cover-crop acreage across nine U.S. states.

Focus
Cover-crop useWeather exposure and establishment
Region
Midwest & Great PlainsNine U.S. states
Study period
2005–201814 years of annual observations
Analytical sample
440 counties6,160 county-year observations

The problem

Conservation programs depend on sustained cover-crop use. Severe drought and unusually wet conditions can change both producers’ reasons to plant and the conditions needed for cover crops to establish.

The study examines whether the severity and timing of weather exposure are associated with the share of cropland where cover crops become established.

My contribution

I carried out data preparation, method development, estimation, mapping and figure production, and manuscript drafting.

Coauthored with T. Wang.

Visualizations by Wajdi Belgacem.

Cover-crop adoption

County patterns, 2005 and 2018.

Figure S1: side-by-side county maps of established cover-crop share in 2005 and 2018, with categories from 0–1 percent to 15 percent or more. Grey counties are outside the estimation sample.
Figure S1. County cover-crop adoption in 2005 and 2018. Percentage of classified cropland with established cover crops. Grey counties are outside the estimation sample.
View full-size Figure S1

Key results

Each additional month of exceptional drought in the preceding growing season was associated with a 1.88-percentage-point decrease in established cover-crop share. Extreme and exceptional wetness were associated with increases of 0.47 and 0.54 percentage points per additional month, respectively.

Baseline estimates, Table 2, Model I; April–September exposure in the preceding year.

Analytical approach

  1. Build the analytical dataset

    Combine OpTIS satellite-based cover-crop shares with NOAA/NIDIS moisture indicators and NOAA nClimGrid-Daily weather records in a balanced county-year panel.

  2. Estimate the relationships

    Use correlated random-effects fractional logit with county means, year effects, weather controls, and standard errors clustered by county.

  3. Assess timing and robustness

    Compare growing-season and establishment exposure, and test alternative weather specifications, lagged exposure, and sensitivity to the 2012 weather year.

Weather exposure across counties and seasons

Figure S4: county maps of mean annual extreme degree days above 30 degrees Celsius and annual precipitation. Heat exposure is higher in the southern study counties; precipitation also varies geographically.
Figure S4. Mean annual extreme degree days and precipitation by county. County averages for 2004–2017, corresponding to crop years 2005–2018. Heat exposure above 30 °C uses the sinusoidal method; both measures use NOAA nClimGrid-Daily data. Grey counties are outside the estimation sample.
View full-size Figure S4
Figure S5: time series of mean monthly exposure to extreme and exceptional drought and wetness, comparing the preceding growing season with the cover-crop establishment window. Shaded bands show variation across counties.
Figure S5. Mean seasonal exposure to weather extremes with dispersion bands. Lines show mean exposure across 440 counties for the preceding April–September growing season and October–March establishment window. Shading is ±1 standard deviation across counties, truncated at zero. The horizontal axis shows the preceding year; panel scales differ.
View full-size Figure S5

Practical relevance

The findings can inform conservation-program timing and technical assistance by identifying weather conditions associated with lower cover-crop establishment.

Interpreting the results

Satellite measures capture established cover crops, so planting intentions cannot be separated from establishment success. The estimates describe county-level associations within the study sample, not causal effects on individual producer decisions.