How Much Are Planting Dates for Maize Affected by the Climate Trend? Lessons for Scenario Analysis Using Land Surface Models

Abstract

Process-based land surface models are important tools to study the historical and future effects of climate change and land use change. The planting date has a considerable effect on crop growth and consequently on dynamic parameters used in land surface models, for example albedo and actual evapotranspiration. If planting dates can be related to climate, scenarios can use this relation to estimate planting dates. Such a relation is expected to differ according to agro-ecological zone. In this study, spring and summer maize planting date observations at 188 agricultural meteorological experiment stations of China, as well as monthly weather records, over the years 1992−2010 were used as the data source. In order to quantify the relation between planting dates and climate parameters, growing season monthly average minimum temperature (Tmin), mean temperature (T), and precipitation (P) were used. The time trend analysis of planting dates and weather data, principal component analysis (PCA) of weather data, and multivariate regression of planting dates as affected by weather data were used. Both Tmin and T increased during this period in most zones, whereas precipitation showed no trend. In southwest and northwest China, maize planting dates advanced significantly for both spring and summer maize. However, in the north China plain (summer maize) and northeast China (spring maize), the planting date was significantly delayed. Ordinary least squares multivariate regression models were able to explain 33% and 59% of the variance of planting dates in the southwest China (i.e., the humid subtropics zone) for spring and summer maize, respectively. However, only 3% could be explained in the Loess Plateau. Thus, adjusting planting dates in scenario analysis using land surface models is indicated for some zones, but not others, where socioeconomic factors are dominant.

Keywords

• maize
• planting dates
• principal component analysis
• climate change