This paper introduces the QLSTM-DLEM model, a quantum model combining Quantum Long Short Term Memory (QLSTM) and Dynamic Land Ecosystem Model (DLEM), for predicting nitrous oxide (N2O) emissions in agricultural fields. Given the time series data collected from different bifurcation branches, integrating all the data into a single model becomes challenging. To address this, time series data, accompanied by signatures, is utilized to distinguish data from different branches. The auto-correlation function (ACF), partial auto-correlation function (PACF), data distribution, and joint distribution of the first-order difference illustrate that time series data collected under different initial conditions and parameters exhibit slight differences but share several similar characteristics. Performance evaluation metrics, including mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R 2), are employed to assess the model. The results indicate that the QLSTM-DLEM model outperforms the classical LSTM-DLEM model in terms of generalization and stability, possibly attributed to the parallelism and superposition of quantum computing. Overall, our simulation results demonstrate that the proposed QLSTM-DLEM hybrid model can effectively predict N2O emissions in agricultural fields, with potential applications in environmental monitoring and management.