Recently published paper in Environmental Research Letters https://iopscience.iop.org/article/10.1088/1748-9326/ac2e38
Shenchao Qiao Tsinghua University writes:
“Global food security is an ongoing challenge owing to the continuous rise in the human population.
Evaluation of potential crop yields is therefore critical for global food security assessment and to attain the sustainable development goal of “No Hunger”.
Potential crop yield; the biophysical ‘ceiling’, is determined by variety, climate and ambient CO2. Statistical modelling and process-based crop models have long been used to estimate and evaluate potential yields. However, some limitations of statistical approaches and large uncertainties of model-based results, still remain.
Therefore, a more robust way to estimate potential crop yields globally, as a function of their growth environment, is needed.”
A recently published paper  offers a method to improve and increase the level of robustness of these models.
In this paper, Shenchao Qiao has extended the optimality-based wheat model (“PC” model), from the national to global scale, in order to understand and predict the climatic impacts on global potential wheat yield.
The PC model comprises of two separate modules; carbon assimilation and carbon allocation (see Figure 1).
- The carbon assimilation module combines a parameter-sparse, optimality-based representation of gross primary production (GPP) with a mass balance-based scheme to predict leaf area index dynamic, to derive GPP from climatic variables and CO2
- The carbon allocation module is a data-driven scheme predicting the allocation of GPP to aboveground biomass (AB) and thence to yield.
Figure 1: The structure of the PC model.
Developing the PC model:
Shenchao states “The PC model was initially developed using data from sites in China where wheat was grown under optimal irrigation and fertilization, and has been well tested in irrigated sites of China .”
Shengchao’s new study extended the original version of PC model to include a scheme to account for water limitation and succeeded in simulating wheat potential yield for rainfed regions also.
Testing the PC model:
Using this extended PC model, the spatial and temporal pattern of global potential wheat yield was predicted. This was then followed by assessing the climatic impacts on potential wheat yield.
The estimations show the PC model captured the magnitude and spatial pattern of potential wheat yield in the sample year 2000 (EARTHSTAT observations) better than the process-based models included in ISIMIP (Inter Sectorial Impact Model Intercomparison Project). (see Figure 2).
Figure 2: EARTHSTAT observation vs Multi-model comparison of potential yield in the year 2000.
Comparing observations and the PC model:
The assessments of climatic impacts (Figure 3) show high temperatures have negatively affected wheat yield over much of the world.
Figure 3 Partial Pearson correlations of (a-d) actual and (e-h) potential wheat yields from 1981 to 2016 with climate and year.
In the observations,
- greater solar radiation is associated with higher yields in humid regions, but lower yields in arid regions.
- greater precipitation is associated with lower yields in humid regions.
These affects are captured by the PC model as well as the positive CO2 fertilization effects on wheat yields.
However, the stronger positive trends in “actual” compared to “potential wheat yields” found in many regions, imply there has also been a positive effect through crop breeding and improved management on actual wheat production.
Shenchaos’ study demonstrates that robust models can be developed using EEO concepts and that this dual crop modelling approach is a useful addition to the toolkit for assessing global environmental change impacts on the growth and yield of arable crops.
Figures courtesy of Shenchao Qiao.
“Optimality-based modelling of climate impacts on global potential wheat yield” Environmental Research Letters https://iopscience.iop.org/article/10.1088/1748-9326/ac2e38
 “Extending a first-principles primary production model to predict wheat yields” Agricultural and Forest Meteorology, https://doi.org/10.1016/j.agrformet.2020.107932.