Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture
Abstract
:1. Introduction
2. Materials and Methods
- SSP3-7.0: A high-emission scenario characterized by regional rivalry and significant climate impacts.
- SSP5-8.5: A fossil fuel intensive development pathway with very high emissions and extreme warming.
2.1. Study Site Description
2.2. Model Selection Justification
2.3. Data Collection and DSSAT Calibration for Wheat Yield Simulation
2.3.1. Soil Profile Input for DSSAT Calibration
2.3.2. Genetic Coefficients for Wheat Cultivar Calibration
2.4. Statistical and Machine Learning Analysis
- Correlation analysis was conducted to examine the strength and direction of linear relationships between simulated yield and individual climate parameters.
- To evaluate whether the data met the assumptions required for parametric statistical analyses, the Shapiro–Wilk test was employed to assess the normality of the distribution for key continuous variables, including wheat yield, precipitation, maximum and minimum temperature, and solar radiation. This test is widely regarded as one of the most powerful methods for detecting departures from normality, particularly for small to moderate sample sizes. The Shapiro–Wilk test statistic WWW is defined as [35]
- Two different machine learning algorithms producing decision trees were elaborated to stabilize and improve the prediction modeling process:
- Feature importance analysis was conducted using a random forest model to quantify the relative contribution of each climate variable to yield variability [12]. This model generates multiple decision trees using a bagging technique for supervised prediction purposes regarding the variable sets. For the main equation is [39]
- Predictive modeling was trained on each dataset to derive a predictive equation for wheat yield. We employed the eXtreme Gradient Boosting (XGBoost) algorithm [40], a high-performance ensemble ML technique. The model was trained on the climate-yield dataset to derive predictive equations estimating wheat yield under future scenarios. The algorithm function uses the following main equation to avoid overfitting [41]:
3. Results and Discussion
3.1. Simulation and Validation of Crop–Soil Dynamics and Yield Performance
3.1.1. Crop and Soil Dynamics During Development
3.1.2. Comparing Simulated and Observed Phenological Stages
3.2. Crop Yield Under Climate Scenarios Analysis
3.2.1. Descriptive Statistics and Normality Test
3.2.2. Climate Feature Importance for Wheat Yield Across SSP Scenarios
3.2.3. Climate Parameters Correlates with Wheat Production Yield in SSP Scenarios
3.3. Model Performance Evaluation
3.4. Relevance to Sustainable Development Goals
3.5. Limitations of the Study
- The DSSAT model was calibrated using only one wheat variety due to data constraints, limiting applicability across genotypes. Future research should include multiple cultivars to improve representativeness under varying climatic responses.
- Predictor correlation analysis was not conducted due to the dataset’s small size and algorithmic nonlinearity. Future studies should apply multicollinearity diagnostics to strengthen machine learning insights.
- The SSP2-4.5, a moderate-emission scenario representing a “middle-of-the-road” socioeconomic pathway with stabilized radiative forcing at 4.5 W/m2 by 2100, was initially analyzed but excluded due to weak climate–yield associations. While this prevented overinterpretation, it limits representation of moderate climate futures. Extended datasets may better reveal SSP2-4.5 impacts.
- Although global sensitivity analysis methods, such as the Morris or Sobol techniques, were not applied in this study due to computational and scope constraints, their use is recognized as valuable for identifying influential model parameters. Instead, the relative influence of key climatic variables on wheat yield was assessed using machine learning algorithms (random forest and XGBoost), which allowed non-linear relationships and variable importance to be explored. Future integration of global sensitivity methods would enhance understanding of crop model behavior.
- The use of random forest and XGBoost allowed nonlinear climate–yield relationships to be captured effectively through ensemble learning. Future studies may benefit from integrating advanced methods-based interpretability to enhance the transparency and explanatory power of machine learning predictions, particularly in multi-variable climate impact assessments.
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
#/g | Number per unit canopy weight grams |
% | Percent |
°C | Degrees Celsius |
°C.d | Degrees Celsius times day |
CO2 | Carbon dioxide |
CV | Coefficient of variation |
DSSAT | The Decision Support System for Agrotechnology Transfer |
ESGF | Earth System Grid Federation |
g dwt | Grams dewater |
GCM | Global Climate Model |
kg/ha | Kilogram per hectare |
km | Kilometers |
kWh/m2 | Kilowatt-hours per square meter |
m | Meters |
MANT | Mean annual minimum temp |
MAP | Mean annual precipitation |
MAR | Mean annual radiation |
MAXT | Mean annual maximum temp |
mg | Milligrams |
mm | Millimeters |
MOA | Ministry Of Agriculture |
MPI-ESM1-2-HR | Meteorology Earth System High-Resolution Model |
MWI | Ministry of Water and Irrigation |
N | Number of samples |
NARC | National Agricultural Research Center |
NOAA | National Oceanic and Atmospheric Administration |
NRMSE | Normalized root mean square error |
Oi | Observed yield |
PBC | Public Benefit Corporation |
pH | Potential of hydrogen |
Pi | Predicted yield |
QMM | Quantile Mapping Method |
RStudio | R Programming Language Studio |
R2 score | Coefficient of determination |
RMSE | Root mean square error |
SOC | Soil organic carbon |
SSP | Shared socioeconomic pathways |
WPY | Wheat production yield |
XGBoost | EXtreme Gradient Boosting |
Mean value of the predicted and of observed yield |
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Criteria | DSSAT [7,10,20] | APSIM [27,28] | STICS [29,30] |
---|---|---|---|
Validated in arid/semi-arid regions | Yes | Partially * | No |
Supports climate impact analysis (e.g., CMIP/SSP integration) | Yes | Yes | Limited |
Requires extensive site-specific calibration | Moderate | Yes | Yes |
Suitable for crop rotation and complex farming systems | Limited | Yes | No |
Strong in water/nutrient balance simulation | Yes | Yes | Yes |
Detailed phenology and yield simulation | Yes | Yes | Yes |
Documentation and user support availability | Extensive | Moderate | Limited |
Region-specific calibration available for the Middle East | Yes | Limited | No |
Used in recent studies in the Mediterranean/Levant region | Yes | Limited | No |
Language and accessibility barriers | No | No | Yes ** |
Open access or freely available | Yes | Yes | Yes |
Soil Depth cm | LL cm3/cm3 | UL cm3/cm3 | SAT cm3/cm3 | ESW cm3/cm3 | ISW cm3/cm3 | RDIST cm | g/cm3 | pH | OC % |
---|---|---|---|---|---|---|---|---|---|
0–5 | 0.228 | 0.41 | 0.48 | 0.182 | 0.228 | 1 | 1.29 | 7.5 | 15 |
5–15 | 0.228 | 0.41 | 0.48 | 0.182 | 0.228 | 1 | 1.29 | 7.5 | 15 |
15–30 | 0.193 | 0.39 | 0.47 | 0.197 | 0.193 | 0.64 | 1.31 | 7.6 | 10 |
30–45 | 0.2 | 0.35 | 0.45 | 0.15 | 0.2 | 0.41 | 1.37 | 7.8 | 10 |
45–60 | 0.2 | 0.35 | 0.45 | 0.15 | 0.2 | 0.41 | 1.37 | 7.8 | 10 |
60–90 | 0.174 | 0.38 | 0.46 | 0.206 | 0.174 | 0.22 | 1.36 | 7.9 | 10 |
90–120 | 0.2 | 0.379 | 0.54 | 0.179 | 0.2 | 0.12 | 1.38 | 7.9 | 10 |
120–150 | 0.21 | 0.37 | 0.45 | 0.16 | 0.21 | 0.07 | 1.4 | 7.9 | 10 |
Coefficient | Value |
---|---|
P1V | 5 |
P1D | 90.75 |
P5 | 567 |
G1 | 22 |
G2 | 60 |
G3 | 0.5 |
Phint | 119 |
DAP | Growth Stage | Biomass kg/ha | Leaf | Crop Nitrogen | Stress Factor | |||
---|---|---|---|---|---|---|---|---|
LAI * (m2/m2) | Number | kg/ha | % | Water | Nitrogen | |||
0 | Sowing | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
5 | Germinate | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
10 | Emergence | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
70 | Terminal Spikelet | 1069 | 0.58 | 6.8 | 11 | 1 | 0 | 0 |
90 | End of Vegetative Stage | 3393 | 2.08 | 9.4 | 33 | 1 | 0 | 0 |
102 | End of Ear Growth | 5794 | 1.88 | 9.4 | 30 | 0.5 | 0 | 0 |
114 | Beginning of Grain Filling | 8499 | 1.68 | 9.4 | 52 | 0.6 | 0.03 | 0.01 |
116 | Harvest | 8642 | 1.59 | 9.4 | 57 | 0.7 | 0 | 0 |
Variable | Description | Simulated | Measured |
---|---|---|---|
Emergence (DAP) | Number of days between sowing and the appearance of the seedling above ground | 10 | 7 |
Anthesis (DAP) | Number of days from sowing until flowering begins | 105 | 110 |
Maturity (DAP) | Number of days from sowing until grain development is complete | −99 | 136 |
Product wt (kg dm/ha) | Final economic yield (e.g., grain) dry matter per hectare, with no post-harvest losses included | 2601 | 2460 |
Product unit weight (g dm) | Dry weight of a single grain/unit, in grams; important for yield component analysis | 0.018 | 0.062 |
Product number (no/m2) | Number of final products (e.g., grains) per square meter | 14,689 | 4000 |
Product harvest index (ratio) | Ratio of economic yield (e.g., grain) to total above-ground biomass; HI = Product wt/Biomass; indicates resource allocation efficiency | 0.3 | 0.25 |
Canopy (tops) wt (kg dm/ha) | Dry weight of above-ground plant parts | 8642 | 9699 |
Vegetative wt (kg dm/ha) | Dry weight of leaves and stems only | 6041 | 7239 |
SSPs | Statistical Parameter | WPY (kg/ha) | MAP (mm) | MAR (kWh/m2) | MAXT (°C) | MANT (°C) |
---|---|---|---|---|---|---|
SSP3-7.0 | Mean ± STDV | 1119 ± 412 | 0.49 ± 0.19 | 18.9 ± 0.09 | 25.1 ± 1.19 | 13.2 ± 1.62 |
CV | 0.37 | 0.38 | 0.00 | 0.05 | 0.12 | |
Skewness | 0.05 | 0.70 | −0.23 | 0.13 | 0.37 | |
Minimum | 151 | 0.19 | 18.7 | 22.8 | 10.5 | |
Maximum | 2062 | 1.05 | 19.1 | 27.6 | 16.9 | |
W | 0.98731 | 0.95938 | 0.97048 | 0.98358 | 0.98118 | |
p-value | 0.77190 | 0.30858 | 0.14060 | 0.57480 | 0.45790 | |
SSP5-8.5 | Mean ± STDV | 1048 ± 446 | 0.49 ± 0.19 | 18.9 ± 0.08 | 25.9 ± 1.42 | 14.0 ± 2.28 |
CV | 0.43 | 0.39 | 0.00 | 0.05 | 0.16 | |
Skewness | −0.51 | 0.57 | 0.04 | 0.03 | 0.24 | |
Minimum | 0 | 0.16 | 18.8 | 23.3 | 10.5 | |
Maximum | 1820 | 1.08 | 19.1 | 28.9 | 19.0 | |
W | 0.96642 | 0.97547 | 0.95060 | 0.98608 | 0.97502 | |
p-value | 0.08751 | 0.24890 | 0.10426 | 0.70720 | 0.23660 |
SSPs | Years | R2 Score | Yield Equations Obtained by XGBoost |
---|---|---|---|
SSP3-7.0 | 2030–2060 | 0.65 | |
2070–2100 | 0.73 | ||
SSP5-8.5 | 2030–2060 | 0.81 | |
2070–2100 | 0.74 |
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El-Mahroug, S.E.; Suleiman, A.A.; Zoubi, M.M.; Al-Omari, S.; Abu-Afifeh, Q.Y.; Al-Jawaldeh, H.F.; Alta’any, Y.A.; Al-Nawaiseh, T.M.F.; Obeidat, N.; Alsoud, S.H.; et al. Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture. AgriEngineering 2025, 7, 156. https://doi.org/10.3390/agriengineering7050156
El-Mahroug SE, Suleiman AA, Zoubi MM, Al-Omari S, Abu-Afifeh QY, Al-Jawaldeh HF, Alta’any YA, Al-Nawaiseh TMF, Obeidat N, Alsoud SH, et al. Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture. AgriEngineering. 2025; 7(5):156. https://doi.org/10.3390/agriengineering7050156
Chicago/Turabian StyleEl-Mahroug, Safa E., Ayman A. Suleiman, Mutaz M. Zoubi, Saif Al-Omari, Qusay Y. Abu-Afifeh, Heba F. Al-Jawaldeh, Yazan A. Alta’any, Tariq M. F. Al-Nawaiseh, Nisreen Obeidat, Shahed H. Alsoud, and et al. 2025. "Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture" AgriEngineering 7, no. 5: 156. https://doi.org/10.3390/agriengineering7050156
APA StyleEl-Mahroug, S. E., Suleiman, A. A., Zoubi, M. M., Al-Omari, S., Abu-Afifeh, Q. Y., Al-Jawaldeh, H. F., Alta’any, Y. A., Al-Nawaiseh, T. M. F., Obeidat, N., Alsoud, S. H., Alshoshan, A. M., Al-Shibli, F. M., & Ta’any, R. (2025). Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture. AgriEngineering, 7(5), 156. https://doi.org/10.3390/agriengineering7050156