Using Statistical Learning Algorithms To Predict Cover Crop Biomass And Cover Crop Nitrogen Content

Cereal rye (Secale cereale sp.) is a cover crop species known to improve soil and water quality. Late-season biomass production is information growers need to maximize cover crop benefits and schedule field operations. Statistical learning, built upon statistical and computational algorithms that "learn" from data, may help to improve predictions of cover crop biomass as a function of initial soil inorganic nitrogen levels. Three models-Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, and Random Forest (RF)-were trained and optimized on a 3-yr allometric and remote sensing dataset of cereal rye responses to N fertilization in the mid-Atlantic northern and southeastern United States. Shoot biomass (mean, 9,800 kg ha(-1)) was accurately predicted with a RF model (RMSE, 2,039 kg ha(-1)). Targeting shoot N content (mean, 107 kg ha(-1)), on the other hand, LASSO made accurate and more stable predictions (RMSE, 34 kg ha(-1)). Early-season information (cover crop C/N ratio, tiller counts, and ground-sensed normalized difference vegetation index) contributed to enhancing biomass and N content predictions. A final test on untrained data revealed that 92 and 73% of the predictions from either algorithm corresponded to ground-truthed biomass and shoot N content observed under different N regimes. Modern data-intensive approaches, such as statistical learning, show promise to characterize end-season performance of a cover crop and may contribute to better farm decision-making.