Climate change has renewed interest in the production capacity of agriculture. Few researchers paid attention to price policy and heteroscedasticity in yield model. We incorporate rice price policy into the yield model at the expected price using a Tobit procedure and take Kalman filter theory to explore useful information, and then estimate the rice yield response to climate and rice price using a spatial autoregressive combined model in high-latitude regions of China from 1992 to 2018. Meanwhile, we apply two different Breusch-Pagan tests to examine heteroscedasticity. Our results suggest that spatial correlation of the error term is a more critical source of heteroscedasticity and cannot be completely solved by only allowing spatially autocorrelated errors due to possible technology diffusion effects. The results also show that rice price support policy is useful for constructing rice expected prices, and the price elasticities of rice and corn on rice yield are 0.194 and -0.097, respectively. Among climate variables, the total growing degree days in the growing season has positive effects, and monthly accumulated growing degree days also matter, especially in June. Precipitation in July and August has a significant effect with an inverse U shape. Projections of future climate change suggest that rice yield will mainly increase, ranging from 0.095% to 1.769%, but the rate of increase in yield will slow down in the higher-rate global warming. This study shows how price policy could be incorporated into yield response model and highlights the importance of climate factors and crop price policy for rice yield.

Purpose: Fewer researchers have investigated the climatic and economic drivers of land-use change simultaneously and the interplay between drivers. This paper aims to investigate the nonlinear and interaction effects of price and climate variables on the rice acreage in high-latitude regions of China.Design/methodology/approach: This study applies a multivariate adaptive regression spline to characterize the effects of price and climate expectations on rice acreage in high-latitude regions of China from 1992 to 2017. Then, yield expectation is added into the model to investigate the mechanism of climate effects on rice area allocation.Findings: The results of importance assessment suggest that rice price, climate and total agricultural area play an important role in rice area allocation, and the importance of temperature is always higher than that of precipitation, especially for minimum temperature. Based on the estimated hinge functions and coefficients, it is found that total agricultural area has strong nonlinear and interaction effects with climate and price as forms of third-order interaction. However, the order of interaction terms reduces to second order after absorbing the expected yield. Additionally, the marginal effects of driven factors are calculated at different quantiles. The total area shows a positive and increasing marginal effect with the increase of total area. But the positive impact of price on the rice area can only be observed when price reached 50% or higher quantiles. Climate variables also show strong nonlinear marginal effects, and most climatic effects would disappear or be weakened once absorbing the expected rice yield. Expected yield is an efficient mechanism to explain the correlation between crop area and climate variables, but the impact of minimum temperature cannot be completely modeled by the yield expectation.Originality/value: To the best of the authors’ knowledge, this is the first study to examine the nonlinear response of land-use change to climate and economic in high-latitude regions of China using the machine learning method.