Predicting cropland and fertilizer consumption models and their effect on crop production in interior Jiangsu Province: a distributed autoregressive lag method - Farheen Solangi; Xingye Zhu; Kashif Ali Solangi; Shahneela Khaskhali; Haijun Yan (Cogent Food & Agriculture)
Monitoring crop production has a direct effect on national and global economies and plays a significant role in food security. This study creates a possible autoregressive integrated moving average (ARIMA) model that can estimate the past (2010 to 2022) and future trends (2023 to 2035) for cultivated cropland and fertilizer consumption and their effects on rice and wheat production. The study results demonstrated past and future trends for different variables such as cultivated cropland, fertilizer consumption and rice, and wheat production over time. Based on the ARIMA model analysis, a 2.4% and 113% total reduction in cropland and fertilizer consumption over the next 13 years respectively was predicted. Over the next 13 years, the production of major crops, specifically rice and wheat, is expected to increase by 12.4% and 25.9%, respectively. However, the multiple linear regression model showed a significant change for dependent variables such as cropland and fertilizer consumption, with R2 values of 61% and 74%, respectively, for rice and wheat production. The predictive results from the ARIMA model analysis possibly showed an increasing trend for estimating crop yields, with a minor change in cultivated cropland and highly decreased fertilizer consumption. These results highlight that higher crop production can be achieved with less cropland and with minor fertilizer inputs.