Projecting future changes in crop yield usually relies on process-based crop models, but the associated uncertainties (i.e. the range between models) are often high. In this study, a Machine Learning (i.e. Random Forest, RF) based observational constraining approach is proposed for reducing the uncertainties of future maize yield projections by seven process-based crop models. Based on the observationally constrained crop models, future changes in yield average and yield variability for the period 2080–2099 are investigated for the globe and top ten producing countries. Results show that the uncertainties of crop models for projecting future changes in yield average and yield variability can be largely reduced by 62% and 52% by the RF-based constraint, respectively, while only 4% and 16% of uncertainty reduction is achieved by traditional linear regression-based constraint. Compared to the raw simulations of future change in yield average (−5.13 ± 18.19%) and yield variability (−0.24 ± 1.47%), the constrained crop models project a much higher yield loss (−34.58 ± 6.93%) and an increase in yield variability (3.15 ± 0.71%) for the globe. Regionally, the constrained models show the largest increase in yield loss magnitude in Brazil, India and Indonesia. Our results suggest more agricultural risks under climate change than previously expected after observationally constraining crop models. The results obtained in this study point to the importance for observationally constraining process crop models for robust yield projections, and highlight the added value of using Machine Learning for reducing the associated uncertainties.
1.Introduction
Understanding how crop yield would change under global warming is critical for adaptation and mitigations. Through representing the key physiological and phenological processes governing crop growth and yield, process-based crop models have been widely used for large-scale projections of crop yield under various climate change scenarios (Asseng et al 2014, Rosenzweig et al 2014, Li et al 2015, Martre et al 2015, Franke et al 2020). However, uncertainties arising from crop model structure, parameters and input data are often high (Asseng et al 2013, Bassu et al 2014, Rosenzweig et al 2014, Mueller et al 2017, Tao et al 2018, Ruane et al 2021), limiting the effective design of adaptation and mitigation strategies. A recent systematic review highlights the need for better understanding and quantifying uncertainty to improve the confidence of crop yield projection under climate change (Chapagain et al 2022). Therefore, how to estimate and narrow down the uncertainty of yield projections has attracted huge attentions in the crop modeling community (Waha et al 2015, Ramirez-Villegas et al 2017, Wang et al 2017, Toreti et al 2020, Muller et al 2021, Ringeval et al 2021).
To date, various approaches have been proposed to minimize such uncertainties through model development, parameter calibration and parameterization improvement (Iizumi et al 2009, Maiorano et al 2017, Makowski 2017, Wang et al 2017, Brown et al 2018, Jägermeyr and Frieler 2018). The uncertainties of crop models can also be reduced by using harmonized meteorological forcing, soil dataset and parameters (Chen and Cournède 2014, Elliott et al 2015, Folberth et al 2016, 2019). Many studies showed that crop model ensemble exhibit better performances than a single or a limited number of models (Bassu et al 2014, Martre et al 2015), especially when fed with harmonized inputs and parameters (Mueller et al 2017). However, such conclusions could be biased, because crop models are not completely independent as some ensemble members share common structure and parameterizations (e.g. EPIC, GEPIC). Therefore, how to better utilize the information of crop model ensemble for improving future yield predictions remain an open question and deserves more investigations.
Theoretically, if there exists a robust relationship across models between the predictand of interest and predictors that can be observed, then the derived relation function combined with observations of predictors can be used to constrain the future projection of the variable of interest. Such idea is often referred to as 'emergent constraint' and has been successfully applied in the climate community for reducing the uncertainty of climate models (Boé et al 2009, Bracegirdle and Stephenson 2012, Cox et al 2013, Klein and Hall 2015, Tsushima et al 2015, Li et al 2017, Eyring et al 2019). Recently, a few studies have tested its efficiency in crop modeling community (Zhao et al 2016, Wang et al 2020). For example, Wang et al (2020) showed that the emergent constraint can reduce the uncertainties of crop models in simulating global yield sensitivity to temperature rise by 12%–54% for maize, rice, soybean and wheat. However, how the constrained models perform in projecting future changes in the year-to-year variability of crop yield remains unknown at the globe scale, despite its great implications for food security.
What's more, though the approach of emergent-constraint has been demonstrated useful, its key limitations should be noted. That is, the relationship function fitted between the predictand and predictor across models is often assumed to be linear. For example, Li et al (2017) built a linear relationship between historical western Pacific precipitation and future Indian summer monsoon rainfall across 24 coupled model intercomparison project phase 5 (CMIP5) models, which is used for constraining the future projection of rainfall change. Similarly, Zhao et al (2016) fitted a linear regression between the historical and future sensitivity of rice yield to warming across 17 models, based on which future rice yield change is constrained. However, we hypothesize that the constraint of future yield changes conditioned on historical observations may exhibit non-linear features, given the complex and nonlinear interactions between crops and the environment.
To address the above gaps, we propose a novel emergent constraint approach based on machine learning for global yield projections. The proposed method advances previous studies in that it can handle non-linear dependences between variables. Besides yield averages, we focus on the changes in the inter-annual variability of yields which has great implications for food security given its relevance to yield stability and crop price. Specifically, the following scientific questions are addressed in this study: (a) How much uncertainties of crop models can be reduced after being observationally constrained using machine learning? (b) How will yield average and variability change by the end of 21st century at the global and country scales? (c) How does the constrained crop models compare with the unconstrained ensemble for future yield projections?
2.Materials and methods
2.1.Maize yield from census data and crop model simulations
Historical census maize yield data for the globe and top ten producing countries are obtained from the Food and Agriculture Organization of the United Nations (FAO) Statistical Database food and agriculture organization corporate statistical database (FAOSTAT) (www.fao.org/faostat/en/#data/QC) for the period 1986–2005 Seven global gridded crop models (GGCMs), including EPIC, GEPIC, IMAGE, LPJ-GUESS, LPJmL, pDSSAT and PEGASUS, driven by five general circulation models, i.e. GFDL-ESM2M, LPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M and HadGEM2-ES, are used to simulate historical (1986–2005) and future (2080–2099) global maize yield. These models differ in their structure, assumptions and parameterizations, and their key information is summarized in table 1. The model simulation outputs (seven GGCMs and two time periods) are downloaded from the Inter-Sector Impact Model Intercomparison Project (ISI-MIP) Fast Track (Rosenzweig et al 2014), which are publicly available from the ISI-MIP Repository at https://data.isimip.org/. These simulations have been widely used for assessing future climate impacts on crop yield, and previous studies showed that uncertainties are several times larger in the highest RCP8.5 than other scenarios (Rosenzweig et al 2014). Therefore, like previous studies (e.g. Schauberger et al 2017), we select RCP8.5 as an example to demonstrate the effectiveness of our approach in reducing the uncertainty of future yield projections. The historical period 1986–2005 is selected to maximize the number of simulations, while the future period 2080–2099 is selected because uncertainties are much greater in the later decades of the 21st century than early periods (Rosenzweig et al 2014). Since these crop models are run at the grid scale driven with gridded data of climate, soil and management, the intra-country variability of environments/crop management are accounted for. These models have been well evaluated and validated separately by each model developing team and collectively by the ISI-MIP community (e.g. Rosenzweig et al 2014, Schauberger et al 2017, Leng and Hall 2020, Yin et al 2022). More information on the model setup and simulation protocol can be found in the literature (Rosenzweig et al 2014, Warszawski et al 2014). To be consistent with the scale of FAO census yield, the simulated maize yields at a spatial resolution of 0.5° × 0.5° are aggregated to the global and country scales, with weights assigned according to the gridded harvest area map from the MIRCA 2000 (Portmann et al 2010).
Table 1.The key information on GGCMs.
| Model | Typea | Soilsb | Root distributionc | Fertilizationd | Irrigatione | Stressesf | Crop cultivarsg | Calibrationh | Key citations |
|---|---|---|---|---|---|---|---|---|---|
| EPIC | Site-based | ISRIC-WISE ROSETTA AWC ALBEDO HYD | LIN W | Automatic N input, PK dynamic application | 90/100/500/50/20 | W, T, H, A, N, P, BD, AL | GDD—fixed | Site-specific NA | Williams (1995), Izaurralde et al (2006) |
| GEPIC | Site-based | ISRIC- WISE | LIN W | NP, dynamic application | 90/100/2000/1000/0 .01 | W, T, H, A, N, P, BD, AL | GDD, 2 cultivars—fixed | Site-specific and global F HIpot | Liu et al (2007), Folberth et al (2012) |
| IMAGE | Global agro-ecological zones; | FAO soil map | W | No nutrient limitation | NA | W, T | GDD + clim. adapt | NA | Leemans and Solomon (1993), Bouwman et al (2006) |
| LPJ-GUESS | Ecosystem | HWSD, STC HYD THM | LIN | NA | 200/90/100/100 | W, T | GDD + V; BT + clim. adap | NA | Sitch et al (2003), Lindeskog et al (2013) |
| LPJmL | Ecosystem | HWSD, STC HYD THM | EXP | NA | 300/90/100/varies | W, T | GDD + V; BT—fixed | Global LAImax HI αa | Waha et al (2012), Bondeau et al (2007) |
| pDSSAT | Site-based | HWSD | EXP | SPAM, dynamic application | 40/80/100 /75 | W, T, H, A, N | GDD, 2–3 cultivars—fixed | Site-specific Na | Jones et al (2003), Elliott et al (2014) |
| PEGASUS | Ecosystem | AWC (ISRIC-WISE) | LIN W | NPK, annual application | 40/90/100 /100 | W, T, H, N, P, K | GDD + clim. adapt | Global β | Deryng et al (2011), Deryng et al (2014) |
Notes: (NA where not applicable). aThe seven models are grouped into three types according to their original purpose, structure, and processes. bHWSD: Harmonized world soil database; ISRIC-WISE: The International Soil Reference and Information Centre-World Inventory of Soil Emission Potentials; ROSETTA: a computer program to estimate soil hydraulic parameters; STC: soil texture classification; AWC: Available Water Capacity; HYD: hydraulic soil parameters; THM: thermal parameters. cLIN: linear; EXP: exponential; W: actuals water depends on water availability in each soil layer. dN (Nitrogen), P (Phosphorus), and K (Potassium); timing: annual or dynamic. eEPIC and GEPIC models: water stress for triggering irrigation (%); irrigation efficiency (%); maximum annual irrigation amount (mm); maximum single irrigation amount (mm); minimum single irrigation amount (mm). LPJ-GUESS, LPJmL, pDSSAT and PEGASUS models: soil moisture measure depth (cm); lower soil moisture threshold for triggering irrigation (%); upper soil moisture threshold for stopping irrigation (%); irrigation efficiency (%). fW: water stress; T: temperature stress; H: specific-heat stress; A: oxygen stress; N: nitrogen stress; P: phosphorus stress; K: potassium stress; BD: bulk density; AL: aluminum stress (based on pH and base saturation). gGDD: Growing Degree Days; Number of cultivars; GDD + V: GDD requirements and vernalization requirements; BT base temperature; fixed: static GDD requirement (no adaptation); clim. adapt: dynamic GDD requirement (adaptation to climate change). hF: fertilizer application rate; HIpot: Potential harvest index; LAImax: maximum LAI; HI: harvest index; αa : leaf-level photosynthesis scaling factor; β: radiation-use efficiency factor.
2.2.Machine-learning based emergent constraint
Unlike calibrations aiming for matching historical simulations closer with observations in terms of distribution and mean, we propose a machine-learning based approach of emergent constraint for constraining or narrowing the uncertainty of crop model simulations for the future period. More specifically, the basic concept of emergent constraint approach is that when we analyze a large ensemble of crop models, a clear relationship may emerge between a variable X in models' simulations in the historical period and a variable Y in models' projections in the future period. When such emergent relationship is identified, we can then plug in actual observations of variable X to narrow the uncertainty of projections for variable Y. Similar ideas have been successfully implemented in the climate modeling community (e.g. Cox et al 2018, Thackeray and Hall 2019, Tokarska et al 2020, Shiogama et al 2022). Like previous studies (e.g. Mueller et al 2017, Schewe et al 2019, Franke et al 2020, Heinicke et al 2022, Yin et al 2022), the FAO yield data is treated as observations and used for constraining GGCMs to reduce their uncertainty in future yield projections.
In our study, uncertainty refers to the range between crop models, calculated as one standard deviation of 35 model simulations, similar to previous studies (Zhao et al 2016, Tokarska et al 2020, Shiogama et al 2022). For each model simulation, two indicators are calculated for analysis, i.e. long-term yield average and interannual yield variability over a 20 year period. Here, the interannual yield variability is measured by the metric of coefficient of variance, which is calculated by dividing the standard deviation of a 20 year yield time series by its mean value.
Previous studies often assume a linear emergent relation across models (Zhao et al 2016, Hall et al 2019, Wang et al 2020), which we hypothesize may not hold in the future under a changing environment. To test our hypothesis, we derive the emergent relation based on Random Forest (RF) which can handle non-linear relations without prior determination of the relationship function (Jeong et al 2016, Hoffman et al 2018, Feng et al 2019). Specifically, the emergent relationship for yield average and yield variability are derived using equation (1)
where RF denotes the RF, 
and 
are the historical and future indicators (i.e. yield average and yield variability) for model 
, respectively. To demonstrate the added value of Machine-learning, the traditional linear regression is adopted for comparison with RF-based constraint:
where 
and 
are the intercept and coefficient, respectively; 
is the error term.
Following Zhao et al (2016), the least squares model error 
is calculated as
and when given 
, the predicted error for 
is
where 
is the variance of 
: 
, 
is the historical indicator for model 
, 
is the average of 
for all models, 
is total number of models, 
is the simulated future indicator and 
is the predicted future indicator based on the emergent 'historical-future' relationship for model 
.
The predicted 
is fit into a Gaussian probability density function (PDF), and its standard deviation of 
: 
is used to derive the constrained PDF. Based on the emergent relationship, the FAO observed yield average and yield variability are used to constrain future projections of maize yield average and yield variability for the globe and top ten producing countries. As for the unconstrained PDF, 
is defined, where 
is the change of 
relative to 
for model 
, 
and 
are the mean and standard deviation of 
, respectively. Based on that, a Gaussian probability distribution is fitted for future changes in yield average and variability in the multi-model ensemble.
3.Results
3.1.Uncertainties of process crop model simulations
Figure 1 shows the boxplots of crop model simulations for the historical and future yield average and variability for the globe and top ten producing countries. It is evident that a large discrepancy exists across crop models for yield simulations both regionally and globally. For the globe as a whole, a large range of yield average from 3.29 t ha−1 to 9.22 t ha−1 is found across models for the historical period, and such discrepancy is further amplified to 2.73–10.04 t ha−1 for the future period. Regionally, the largest discrepancy is observed for India with the range between 0.42 and 11.70 t ha−1, followed by South Africa and Argentina. These revealed ranges across crop model projections point to the importance of reducing the uncertainties for robust yield projections. In general, the ensemble median of crop models tends to match observation more closely than an individual model, consistent with previous studies (Rosenzweig et al 2013, Bassu et al 2014, Li et al 2015, Martre et al 2015, Wang et al 2016). However, such conclusion does not hold at the country scale, and process models show a significant overestimation of yield average in Brazil, Mexico, India, Indonesia and south Africa. Relative to the historical period (1986–2005), the ensemble median of process models simulates a large decrease up to −8.62% for global maize yield during the period 2080–2099. Regionally, negative effects of global warming are projected in low latitude regions while positive effects are simulated in high-latitude regions, consistent with previous studies (Rosenzweig et al 2014). For example, the largest yield decrease is found in USA with a yield loss up to 23.18%, while an increase by 4.99% is projected for Canada. Such regional differences reflect the spatial heterogeneity of cropping system and yield sensitivity to climate change as represented in GGCMs. Indeed, the baseline climate in most of low latitude regions is already quite close to the high-temperature thresholds for suitable production, and thus rising temperatures are detrimental there and vice versa for high latitude region (e.g. Canada).
Similar to yield average, the ensemble median of process model exhibits a promising performance for reproducing historical year-to-year variability of global maize yield. However, in contrast, process models show an underestimation of yield variability in most of major producing countries, including China, Brazil, Mexico, South Africa and Canada. Relative to the historical period, a slight decrease of 0.27% is projected by model ensemble median for future yield variability. Regionally, a mixed pattern of yield variability change is observed across the top ten countries, with a projected increase in USA, France and Canada but a decrease in the remaining countries. However, the uncertainties are also high at the global and country level, and the largest uncertainty is found in South Africa with the range of values between 1.79% and 33.68%. Overall, the revealed large uncertainties highlight the need for observationally constraining crop models for robust projection of future changes in yield average and variability.
3.2.The emergent relationship
Figure 2 shows the scatterplot of projected changes in yield average against historical simulations for the globe and top ten producing countries. In general, models which simulate higher yield in the historical period is also likely to project higher yield in the future period. Such an emergent relation can be well described by both traditional linear regression and RF, with a R2 of 0.81 and 0.97 for the globe, respectively. Similar to global-scale results, robust emergent relationships are detected for all the top ten countries. Compared to linear regression, RF exhibits a higher performance in reproducing the emergent relation. Since the main difference between linear regression and RF is that the latter can capture non-linear relations while the former cannot, the difference in their performances indicates the role of non-linear effects for the emergent constraint. Indeed, a non-linear curve is fitted by RF (red line in figure 2), which outperforms the linear regression (blue line in figure 2).
Similarly, models showing larger yield variability tend to predict more instable yields in the future (figure 3). For the globe as a whole, linear regression and RF exhibit a high R2 of 0.70 and 0.91 for fitting the emergent relation, respectively. Such a high performance is also found for most countries, with the highest R2 of 0.72 and 0.93 by linear regression and RF for China, respectively. In Brazil, however, linear regression fails to capture the emergent relation with a R2 of 0.10, while RF exhibit a high performance with R2 0.86. Overall, our results demonstrate that RF is better than linear regression in describing the emergent relations at the global and country levels (supplementary table S1), which allows for observationally constraining the projections of future changes in yield average and yield variability.
3.3.Constraint on future changes in yield average and yield variability
Figure 4 shows the probability density function (PDF) of the constrained and unconstrained (raw) projections of future changes in yield average for the globe and top ten producing countries. For the globe as a whole, a large uncertainty of 18.19% is found by the unconstrained crop models. After observationally constraining crop models, such uncertainty is reduced to 6.93% by RF, while a slight uncertainty reduction is achieved by linear regression. The significant uncertainty reduction achieved with RF in our study is also higher than most of previous studies using emergent constraint (Cox et al 2013, Wenzel et al 2016, Zhao et al 2016, Eyring et al 2019, Wang et al 2020, Xu et al 2020), demonstrating the added value of using RF for the constraint. Importantly, the constrained models based on linear regression and RF both project a more severe yield loss of −31.01% and −34.58% for the globe, respectively, compared to −5.13% in the unconstrained models. This indicates that previous yield change projections based on raw crop models may have largely underestimated yield loss magnitude (Rosenzweig et al 2014, Mueller et al 2017).
Regionally, larger uncertainties of unconstrained models up to 20.93%–31.22% are found for the top ten producing countries (supplementary table S2). When constrained against observations by RF, the uncertainty of projected yield changes is largely reduced by 31%–81%, while a much smaller uncertainty reduction is obtained by linear regression (−6%–38%). Overall, compared to the raw projections, the constrained models based on RF and linear regression both suggest more yield loss magnitude for most countries, except for France and Canada. For example, maize yield in USA is projected to decrease by −24.70% based on RF, which is five times larger than the loss in the raw crop models (−4.64%). In Brazil, the projected yield loss could even increase from −13.63% in raw models to −82.22% in the RF-based constrained models. Compared to the projections based on linear regression, the RF-based constrained models simulate more yield loss magnitude in USA, China, Brazil, India and Indonesia, but smaller decrease in Mexico, Argentina and South Africa. Such differences between the constrained projections between linear regression and RF indicate the role of non-linear effects in the constraint.
As for yield variability (figure 5), the unconstrained models show an uncertainty range of 1.47% for the globe, which is six times larger than the ensemble mean change value (−0.24%). After applying the emergent constraint by RF, the uncertainty is reduced by half to 0.71%, which triples the magnitude of uncertainty reduction achieved by linear regression. In contrast to the projected decrease in yield variability by the raw models, the constrained models based on RF shows a large increase of yield variability of 3.15%, implying more unstable yield in the future. Such a disagreement in the change direction of yield variability cannot be obtained, however, when using the linear regression for the constraint.
For the top ten producing countries, the unconstrained models show diverse behaviors for projecting future changes in yield variability, which ranges from 1.54% to 9.55% across countries (supplementary table S3). The RF-based constraint can well reduce the uncertainty by 41%–80%, much larger than that based on linear regression (−3%–51%). In China, the linear regression-based constraint even leads to an amplification of the uncertainty. Similar to global-scale results, the RF-based constrained models project a future increase in yield variability in most countries. For example, maize yield variability in USA is projected to increase by 0.22% based on RF, while a decrease is simulated by the raw crop models (−0.19%). Given the promising performance of RF in describing the emergent relation as shown in figure 3, the projected increase in yield variability suggests more risks for maize production than previously expected based on the raw crop models.
4.Discussions
It is well known that GGCMs differ in their approaches, assumptions, and structures, and often show large uncertainty in future yield projections (Asseng et al 2013, Bassu et al 2014, Rosenzweig et al 2014, Tao et al 2018). Calibration in a detailed manner would help reduce the uncertainties, but is challenging for seven crop models across the globe. In addition, calibration outcome is sensitive to the choices of parameters to calibrate, data used for calibration, calibration method (e.g. Ordinary Least Square, Markov chain Monte Carlo, Bayesian and Generalized Likelihood Uncertainty Estimation) and etc (Angulo et al 2013, He et al 2017, Seidel et al 2018). Therefore, instead of further recalibrating the seven GGCMs globally, we propose an alternative approach (i.e. emergent constraint) which is efficient to implement and also effective in reducing the uncertainty in future yield projections. Specifically, our approach is based on the fitting of emergent relations that would emerge across the ensemble of crop models. Besides traditional linear regression, we introduce machine-learning (i.e. RF) to demonstrate its added value for the constraint. Our results indicates that both regression and RF can well capture the emergent relations, with RF outperforming regression in the fitting. Importantly, we show that observationally constraining ISI-MIP crop models can largely reduce their uncertainties in future yield projections.
For better interpretation of our findings, however, there are some limitations that should be acknowledged. We also identify several aspects of future research needs as below.
First, we constrain future changes in yield average and yield variability at the country and global scale, ignoring the spatial heterogeneity. This is because FAO is the only publicly available crop yield data, and only global and country-level values are provided. When aggregating the gridded simulations of crop models to global and country scales, grid-specific weights are assigned according to its harvest area, which is hold constant due to the lack of time-varying harvest area maps. Leng and Huang (2017) showed that US maize yield response depends on the spatial distribution of crops. Sloat et al (2020) further demonstrated the importance of crop migration in mitigating the adverse climate impacts. However, how the changes in cropping area would affect the constraint of yield changes requires further investigation, and is not within the goal of our study as process models adopted in our study does not account for such dynamics.
Second, the uncertainties related to the effects of climate change on maize yield have not been analyzed. As shown in previous studies (Zhao et al 2016, Wang et al 2020), the uncertainties of crop yield responses to temperature increase have been successfully investigated with the emergent constraint approach. Besides, there are large changes of climate factors and their interactions in the future scenarios, including temperature, precipitation and CO2, which would disturb the estimation of individual factor (Rosenzweig et al 2014). Therefore, we focus on the future changes of yield and yield variability and using the emergent constraint to reduce derived uncertainties.
Third, the uncertainty from observed data is not completely sampled. As demonstrated in the literature (Cox et al 2013, Zhao et al 2016, Eyring et al 2019), emergent constraint is conducted in two steps: (a) first, develop the emergent- relationship across models; (b) second, use observations to constrain models for future projections. So, there are two sources of uncertainties that could affect the performance of emergent constraint. Zhao et al (2016) collected observed temperature sensitivities of rice yield from 83 field experiments based on which the uncertainties from the observations can be derived. Due to lack of such information for field-level yield observations worldwide, we focus on the uncertainty in emergent relationship. To reduce such uncertainty, we need to explore a robust relationship. To address this, we use RF for deriving the emergent relationship, which lead to smaller uncertainties than using traditional linear regression.
Fourth, it is well-known that crop yield depends on many secondary traits and bio-physical processes. Therefore, it is important to constrain its primary drivers, processes and secondary traits to advance our scientific understandings of future changes in crop yield. Here, we constrain crop yield only, because crop yield is the priority variable requested to be outputted by each crop modeling team within ISI-MIP, while other variables related to crop growth, phenology and other secondary traits are not routinely archived. What's more, observations on crop yield are available worldwide from the FAO database, while observations of other secondary traits are not readily available worldwide. Future efforts should be devoted to constraining both gain yield and its secondary traits, once both simulations and observations are available. Fifth, climate change impacts on crop yield could be further modulated by various adaptation strategies. For example, cultivar choice has been shown to be effective in reducing the negative effects of future warming (Liu et al 2013, Waha et al 2013, Huang et al 2020). Besides cultivar improvement, other adaptation strategies such as irrigation, fertilization, soil mulching and crop migration could further modulate crop yield response to climate change (Karlen et al 2013, Qin et al 2015, Seifert and Lobell 2015, Troy et al 2015, Sloat et al 2020). To account for all possible factors influencing crop yield is not within the scope of this study. Rather, this study aims to propose an efficient and effective approach for reducing the uncertainty of crop models in future yield projections. In our study, we use the simulations from ISI-MIP, which have been widely used in the community for assessing future climate impacts on crop yield (Rosenzweig et al 2014, Warszawski et al 2014, Schauberger et al 2017). Unfortunately, the simulation protocol is not designed for evaluating adaptation effects. Therefore, the output of our approach would depend on the specific environmental, crop management and genotypic factors represented in these specific simulations.
Despite the above limitations, our study contributes to literature by demonstrating the added value of using Machine Learning for observationally constraining process crop models for projecting future changes in yield average and variability. Indeed, combining machine learning and process models lead to a large reduction of uncertainties for future yield projections, with significant influence on the changes in yields not only for the long-term average but also for the year-to-year variability. Our proposed approach is demonstrated to be efficient and effective, and can therefore be easily extended to other crops, variables, periods, regions and environments of interest, once ensemble simulations and observations are both available.
5.Conclusions
Process-based crop models have been widely used for projecting future yield changes, but often show substantial uncertainties. In this study, we adopt the approach of emergent constraint for observationally constrain the projections of 35 crop model simulations for future changes in global maize yield average and yield variability. Instead of assuming a linear emergent relationship as pursued in previous studies, we propose a new approach based on Machine-Learning, which can handle non-linear relations between the predicant and predictors. Compared with the traditional approach, the RF shows a much better performance in modeling the emergent relations, leading to a large reduction of the uncertainty of crop model projections of both yield average and yield variability at the global and country scales. Specifically, the uncertainty range of constrained crop models is reduced by 62% and 52% for projecting future changes in global maize yield and yield variability, respectively, which more than triple that achieved with traditional linear regression. Based on the observationally constrained models by RF, a more severe yield loss (−34.58 ± 6.93%) and larger year-to-year variability (3.15 ± 0.71%) are simulated than that based on the raw models by the end of 21st century. Regionally, the constrained models show the largest increase in yield loss magnitude in Brazil, India and Indonesia compared to the raw models. Our results suggest that previous studies based on the raw crop models may have largely underestimated the agricultural risks under climate change. This study highlights the added value of using machine learning for observationally constraining process crop models for more robust projections of future yield changes.
Acknowledgments
This research was funded by the National Natural Science Foundation of China (No. 42077420) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA28060100).
Data availability statement
The data that support the findings of this study are openly available. Census data on annual maize yields of the globe and top ten producing countries are obtained from the Food and Agriculture Organization of the United Nations (FAO) FAOSTAT database (www.fao.org/faostat/en/#data/QC). The simulated yields by process-based models are from the Inter-Sector Impact Model Intercomparison Project (ISI-MIP) Fast Track project at www.isi-mip.org. The crop harvest area map is from www.uni-frankfurt.de/45218023/MIRCA.
The data that support the findings of this study are available upon reasonable request from the authors.
