Optimality-based modelling of climate impacts on global potential wheat yield

Abstract

Evaluation of potential crop yields is important for global food security assessment because it represents the biophysical ‘ceiling’ determined by variety, climate and ambient CO2. Statistical approaches have limitations when assessing future potential yields, while large differences between results obtained using process-based models reflect uncertainties in model parameterisations. Here we simulate the potential yield of wheat across the present-day wheat-growing areas, using a new global model that couples a parameter-sparse, optimality-based representation of gross primary production (GPP) to empirical functions relating GPP, biomass production and yield. The model reconciles the transparency and parsimony of statistical models with a mechanistic grounding in the standard model of C3 photosynthesis, and seamlessly integrates photosynthetic acclimation and CO2 fertilization effects. The model accurately predicted the CO2 response observed in FACE experiments, and captured the magnitude and spatial pattern of EARTHSTAT ‘attainable yield’ data in 2000 CE better than process-based models in ISIMIP. Global simulations of potential yield during 1981–2016 were analysed in parallel with global historical data on actual yield, in order to test the hypothesis that environmental effects on modelled potential yields would also be shown in observed actual yields. Higher temperatures are thereby shown to have negatively affected (potential and actual) yields over much of the world. Greater solar radiation is associated with higher yields in humid regions, but lower yields in semi-arid regions. Greater precipitation is associated with higher yields in semi-arid regions. The effect of rising CO2 is reflected in increasing actual yield, but trends in actual yield are stronger than the CO2 effect in many regions, presumably because they also include effects of crop breeding and improved management. We present this hybrid modelling approach as a useful addition to the toolkit for assessing global environmental change impacts on the growth and yield of arable crops.

Optimality principles explaining divergent responses of alpine vegetation to environmental change

“…The mass balance‐based water limitation requires that transpiration (accompanied by carbon assimilation) should be matched by the water supply from precipitation (Qiao et al, 2020, 2021). We assume that vegetation makes use of a certain fraction ( f 0 ) of precipitation to maintain its capacity for photosynthesis.where T is the total transpiration in the growing season (mm year −1 ), PREC is annual total precipitation (mm year −1 ) and f 0 is the ratio of precipitation to transpiration (dimensionless), which depends on the partitioning of evapotranspiration (ET) and precipitation.…”

“…Derived from equation ( 16) and (19), based on the water mass-balance, water limited vegetation cover (fAPARwater) can be calculated as: This framework posits that peak vegetation cover should be determined by the lesser of the water and energy limited vegetation cover 61 . Fmax can then be expressed as:…”

“…The mass balance-based water limitation requires that transpiration (accompanied by carbon assimilation) should be matched by the water supply from precipitation (Qiao et al, 2020(Qiao et al, , 2021. We assume that vegetation makes use of a certain fraction (f 0 ) of precipitation to maintain its capacity for photosynthesis.…”

“…EEO relies on natural selection eliminating uncompetitive plant strategies, assuming that plants acclimate or adapt to their environment on both shorter (days to months) eco-physiological timescales and on longer demographic and evolutionary timescales. An EEO approach to predicting vegetation cover, based on the idea of mass balance and maximum carbon profit, has been successfully applied to both natural vegetation (Yang et al, 2018) and crops (Qiao et al, 2020(Qiao et al, , 2021. A similar EEO approach can be used to explore the impacts of recent climate change on vegetation cover in a simple and transparent way.…”

An Integrated Yield-Based Methodology for Improving Soil Nutrient Management at a Regional Scale

“…Wheat yield may be influenced by many factors, such as soil physical and chemical properties, climate, irrigation, tillage, and so on [5][6][7][8][9][10][11][12]. Since the influence degree of environmental factors in each agricultural area is not identical, the main limiting factors for crop yield are usually different.…”