Abstract
As the most important food crop across the world, with continuous increase in world population and steady declining farmlands, wheat has been attracting academic attention for improving its yield or potential in the future particularly under global warming. Therefore, analyzing the yield or potential of wheat at global level relevant to greenhouse gas effect is of great significance to direct future production of wheat in the world. However up to now, there are relatively few reports on potential yield of world wheat projected using ‘time series’ approach like ARIMA (Auto-regressive Integrated Moving Average) model. Thus in this paper, the crop potential yield of world wheat during 2019 to 2028 is projected using ARIMA model based on the yields from 1961 to 2018. Our results show that during 2019 to 2028, the average yields of world wheat are projected to increase from 3569 to 4257 kg ha-1 while top yields of world wheat from 9852 to 11246 kg ha-1. Annual global mean temperatures are projected to increase from 15.05 to 15.31°C. Global warming exerts positive effect on average yield of world wheat while negative effect on the top yield in 1961 to 2018 and 2028. Our study concluded that for world wheat production in 2019 to 2028, the opportunities for improving production should be mainly dependent on the advantage of highyield countries as the yield is still in low place before the turn-point of S-shaped curve in long-term trend affected partly by greenhouse gas effect.
Highlights
As the most important food crop across the world, with continuous increase in world population and steady declining farmlands, wheat has been attracting academic attention for improving its yield or potential in the future under global warming
Recent studies to determine the wheat yield and its potential through modelling have provided a number of important incites e.g. The accuracyand efficiency of optimization algorithms was done by comparing the POWELL and SCE-UA method to predict the regional winter wheat yield, the comparison shows that POWELL algorithm performs better than SCEUA due to the high assimilation accuracy and much higher running efficiency (Tian et al, 2013)
The DSSAT model, integrated with calibrated Hargreaves ET model and dynamic crop coefficient, was run with the generated weather data to predict the potential yield and crop water requirement of winter wheat in the Huang-HuaiHai Plain in China; the models suggested that the spatial distribution of potential yield in the future was characterized by an increasing trend from the northwest inland to the southeast coast (Tang et al, 2018)
Summary
As the most important food crop across the world, with continuous increase in world population and steady declining farmlands, wheat has been attracting academic attention for improving its yield or potential in the future under global warming. Annual global mean temperature (°C), historic or statistical data of average and top yields (at national level) of world wheat from 1961 to 2018 is used for projecting and analyzing their futures under global warming.
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