|
|
Xiao, D. P., & Tao, F. L. (2016). Contributions of cultivar shift, management practice and climate change to maize yield in North China Plain in 1981-2009. International Journal of Biometeorology, 60(7), 1111–1122.
Abstract: The impact of climate change on crop yield is compounded by cultivar shifts and agronomic management practices. To determine the relative contributions of climate change, cultivar shift, and management practice to changes in maize (Zea mays L.) yield in the past three decades, detailed field data for 1981-2009 from four representative experimental stations in North China Plain (NCP) were analyzed via model simulation. The four representative experimental stations are geographically and climatologically different, represent the typical cropping system in the study area, and have more complete weather/crop records for the period of 1981-2009. The results showed that while the shift from traditional to modern cultivar increased yield by 23.9-40.3 %, new fertilizer management increased yield by 3.3-8.6 %. However, the trends in climate variables for 1981-2009 reduced maize yield by 15-30 % in the study area. Among the main climate variables, solar radiation had the largest effect on maize yield, followed by temperature and then precipitation. While a significant decline in solar radiation in 1981-2009 (maybe due to air pollution) reduced yield by 12-24 %, a significant increase in temperature reduced yield by 3-9 %. In contrast, a non-significant increase in precipitation during the maize growth period increased yield by 0.9-3 % at three of the four investigated stations. However, a decline in precipitation reduced yield by 3 % in the remaining station. The study revealed that although the shift from traditional to modern cultivars and agronomic management practices contributed most to the increase in maize yield, the negative impact of climate change was large enough to offset 46-67 % of the trend in the observed yields in the past three decades in NCP. The reduction in solar radiation, especially in the most critical period of maize growth, limited the process of photosynthesis and thereby further reduced maize yield.
|
|
|
|
Luo, K., Tao, F., Deng, X., & Moiwo, J. P. (2017). Changes in potential evapotranspiration and surface runoff in 1981-2010 and the driving factors in Upper Heihe River Basin in Northwest China. Hydrol. Process., 31(1), 90–103.
Abstract: Changes in potential evapotranspiration and surface runoff can have profound implications for hydrological processes in arid and semiarid regions. In this study, we investigated the response of hydrological processes to climate change in Upper Heihe River Basin in Northwest China for the period from 1981 to 2010. We used agronomic, climatic and hydrological data to drive the Soil and Water Assessment Tool model for changes in potential evapotranspiration (ET0) and surface runoff and the driving factors in the study area. The results showed that increasing autumn temperature increased snow melt, resulting in increased surface runoff, especially in September and October. The spatial distribution of annual runoff was different from that of seasonal runoff, with the highest runoff in Yeniugou River, followed by Babaohe River and then the tributaries in the northern of the basin. There was no evaporation paradox at annual and seasonal time scales, and annual ET0 was driven mainly by wind speed. ET0 was driven by relative humidity in spring, sunshine hour duration in autumn and both sunshine hour duration and relative humility in summer. Surface runoff was controlled by temperature in spring and winter and by precipitation in summer (flood season). Although surface runoff increased in autumn with increasing temperature, it depended on rainfall in September and on temperature in October and November. Copyright (C) 2016 John Wiley & Sons, Ltd.
|
|
|
|
Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R. P., et al. (2015). Multimodel ensembles of wheat growth: many models are better than one. Glob. Chang. Biol., 21(2), 911–925.
Abstract: Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.
|
|
|
|
Tao, F., Zhang, S., Zhang, Z., & Rötter, R. P. (2014). Maize growing duration was prolonged across China in the past three decades under the combined effects of temperature, agronomic management, and cultivar shift. Glob. Chang. Biol., 20(12), 3686–3699.
Abstract: Maize phenology observations at 112 national agro-meteorological experiment stations across China spanning the years 1981-2009 were used to investigate the spatiotemporal changes of maize phenology, as well as the relations to temperature change and cultivar shift. The greater scope of the dataset allows us to estimate the effects of temperature change and cultivar shift on maize phenology more precisely. We found that maize sowing date advanced significantly at 26.0% of stations mainly for spring maize in northwestern, southwestern and northeastern China, although delayed significantly at 8.0% of stations mainly in northeastern China and the North China Plain (NCP). Maize maturity date delayed significantly at 36.6% of stations mainly in the northeastern China and the NCP. As a result, duration of maize whole growing period (GPw) was prolonged significantly at 41.1% of stations, although mean temperature (Tmean) during GPw increased at 72.3% of stations, significantly at 19.6% of stations, and Tmean was negatively correlated with the duration of GPw at 92.9% of stations and significantly at 42.9% of stations. Once disentangling the effects of temperature change and cultivar shift with an approach based on accumulated thermal development unit, we found that increase in temperature advanced heading date and maturity date and reduced the duration of GPw at 81.3%, 82.1% and 83.9% of stations on average by 3.2, 6.0 and 3.5 days/decade, respectively. By contrast, cultivar shift delayed heading date and maturity date and prolonged the duration of GPw at 75.0%, 94.6% and 92.9% of stations on average by 1.5, 6.5 and 6.5 days/decade, respectively. Our results suggest that maize production is adapting to ongoing climate change by shift of sowing date and adoption of cultivars with longer growing period. The spatiotemporal changes of maize phenology presented here can further guide the development of adaptation options for maize production in near future.
|
|
|
|
Bassu, S., Brisson, N., Durand, J. - L., Boote, K., Lizaso, J., Jones, J. W., et al. (2014). How do various maize crop models vary in their responses to climate change factors. Glob. Chang. Biol., 20(7), 2301–2320.
Abstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.
|
|
