Impact of Cover Crop Planting and Termination Dates on Arthropod Activity in the Following Corn

Enhancing estimation of cover crop biomass using field-based high-throughput phenotyping and machine learning models

Incorporating cover crops into cropping systems offers numerous potential benefits, including the reduction of soil erosion, suppression of weeds, decreased nitrogen requirements for subsequent crops, and increased carbon sequestration. The aboveground biomass (AGB) of cover crops strongly influences their performance in delivering these benefits. Despite the significance of AGB, a comprehensive field-based high-throughput phenotyping study to quantify AGB of multiple cover crops in the U.S. Midwest has not been found. This study presents a two-year field experiment carried out in Eastern Nebraska, USA, to estimate AGB of five different cover crop species [canola (Brassica napus L.), rye (Secale cereale L.), triticale (Triticale × Triticosecale L.), vetch (Vicia sativa L.), and wheat (Triticum aestivum L.)] using high-throughput phenotyping and Machine Learning (ML) models. Destructive AGB sampling was performed three times during each spring season in 2022 and 2023. An array of morphological, spectral, thermal, and environmental features from the sensors were utilized as feature inputs of ML models. Moderately strong linear correlations between AGB and the selected features were observed. Four ML models, namely Random Forests Regression (RFR), Support Vector Regression (SVR), Partial Least Squares Regression (PLSR), and Artificial Neural Network (ANN), were investigated. Among the four models, PLSR achieved the highest Coefficient of Determination (R2) of 0.84 and the lowest Root Mean Squared Error (RMSE) of 892 kg/ha (Normalized RMSE (NRMSE) = 8.87%), indicating that PLSR could be the most appropriate method for estimating AGB of multiple cover crop species. Feature importance analysis ranked spectral features like Normalized Difference Red Edge (NDRE), Solar-induced Fluorescence (SIF), Spectral Reflectance at 485 nm (R485), and Normalized Difference Vegetation Index (NDVI) as top model features using PLSR. When utilizing fewer feature inputs, ANN exhibited better prediction performance compared to other models. Using morphological and spectral parameters as input features alone led to a R2 of 0.80 and 0.77 for AGB prediction using ANN, respectively. This study demonstrated the feasibility of high-throughput phenotyping and ML techniques for accurately estimating AGB of multiple cover crop species. Further enhancement of model performance could be achieved through additional destructive sampling conducted across multiple locations and years.

Evaluating the impact of at-plant termination of a cereal rye cover crop with different corn planting dates on arthropod activity

Greater cover crop biomass is expected to result in a favorable microhabitat for beneficial arthropods. Natural Resources Conservation Service (NRCS) cover crop termination guidelines are based on the cash crop planting date. Therefore, a delay in cash crop planting could result in greater cover crop biomass. However, studies on delays in cash crop planting and greater cover crop biomass have led to a decrease in cash crop yield. Thus, a field study was conducted in eastern Nebraska over two years to evaluate the impact of early and late corn planting dates with at-plant cover crop terminations on pest potential, beneficial arthropod activity, and agronomic parameters. To measure arthropod activity and pests in the system, pitfall traps, and corn injury assessments were performed during the early stages of corn development. A total of 11,054 and 43,078 arthropods were collected in 2020 and 2021, respectively. The results have shown no impact of the corn planting dates with at-plant cover crop termination on arthropods but identified that cereal rye cover crop supports greater Araneae activity while its alternative prey varied when compared to the no-cover treatment. Significant yield penalties were observed when cover crop was used regardless of the corn planting dates. Pest pressure was not significant in any year, however, future research using cereal rye and different cover crop species should be used in this system with an artificial infestation of a pest to be able to evaluate the trade-offs between possible cash crop yield reductions and potential biological control of pests.