Ecological Synchronization or Technological Inertia? Unveiling Structural Dualism in Iran’s Wheat Production Systems and Mapping the 2036 Horizon

Document Type : Original Article

Authors

1 Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.

2 Department of Plant Production and Genetics, Campus of Agriculture and Natural Resources, Razi University, Kermanshah, Iran.

3 Cereal Research Center, Razi University, Kermanshah, Iran.

Abstract

Abstract
Introduction: In Iran, wheat is more than just a crop; it is a strategic commodity with a high national security priority. Despite four decades of efforts toward self-sufficiency, the country's wheat production system continues to face structural challenges such as water stress, land fragmentation, and severe productivity fluctuations. A lack of comprehensive understanding regarding the transmission of climate-policy shocks and the short-term reciprocal interaction between cultivated area and yield has been the Achilles' heel of self-sufficiency programs. This study aims to fill this gap by investigating whether production growth drivers operate identically across the two farming systems (irrigated and rainfed) and determining the production outlook for the 2036 (1415 AH) horizon in light of ecological constraints.
Materials and Methods: Time series data for area, yield, and production spanning from 1982 to 2023 (42 years) served as the basis for analysis. The research methodology involved several analytical layers: 1) Univariate analyses including compound growth rates, stability assessment, and production growth decomposition; 2) Stationarity tests and determination of the order of integration; 3) Selection of optimal lag length and cointegration testing; 4) Specification of the dynamic model and diagnostic validity assessment; and finally, 5) Impulse Response Functions (IRF), Forecast Error Variance Decomposition (FEVD), and variable forecasting.
Findings: Analysis of the compound growth rate over the 42-year period indicates a paradigm shift in Iran's wheat production from horizontal expansion to vertical development strategies. Significant growth in total production (1.79%) and irrigated wheat (1.91%) was primarily driven by improvements in yield per unit area (growing at 1.72%). Diagnostic results revealed that structural shocks have transitory effects, and the system exhibits mean-reverting properties. FEVD analysis uncovered a fundamental divergence between the two systems: in the irrigated system, the cross-share of shocks was less than 0.2%, confirming structural independence and the dominant role of technology. Conversely, in the rainfed system, cultivated area shocks accounted for 20% of yield fluctuations, proving the phenomenon of ecological synchronicity (the simultaneous impact of rainfall on both area and yield). Forecasting results indicate that the total cultivated area will follow a fluctuating downward trend to reach 7.55 million hectares by 2036; however, total production will rise to 23.57 million tons, driven by a surge in irrigated yield (from 3892 kg/ha in the base year to 5104 kg/ha).
Conclusion: The findings of this research reject the area-centered development paradigm, particularly in rainfed regions. The future of wheat production security depends on vertical development and concentrating investment on yield enhancement in irrigated lands. Given the predicted contraction of rainfed area, the optimal strategy for this sector is not area expansion, but rather risk management and shifting cropping patterns toward resilient varieties.

Keywords

Main Subjects


Anderson, W. B., Seager, R., Baethgen, W., Cane, M., & You, L. 2019. Synchronous crop failures and climate-forced production variability. Science Advances, 5, eaaw1976.
Barlow, M., Zaitchik, B., Paz, S., Black, E., Evans, J., & Hoell, A. 2016. A review of drought in the Middle East and southwest Asia. Journal of Climate, 29 (23), 8547-8574.
Bastos, A., Ciais, P., Friedlingstein, P., Sitch, S., Pongratz, J., Fan, L., ... & Zaehle, S. 2020. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Science Advances, 6 (24), eaba2724.
Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. 2015. Time series analysis: forecasting and control. John Wiley & Sons.
Brown, R. L., Durbin, J., & Evans, J. M. 1975. Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society Series B: Statistical Methodology, 37 (2), 149-163.
Burnham, K. P., & Anderson, D. R. (Eds.). 2002. Model selection and multimodel inference: a practical information-theoretic approach. New York, NY: Springer New York.
Cassman, K. G., & Grassini, P. 2020. A global perspective on sustainable intensification research. Nature Sustainability, 3 (4), 262-268.
Cuddy, J. D., & Della Valle, P. A. 1978. Measuring the instability of time series data. Oxford Bulletin of Economics & Statistics, 40 (1), 71-78.
Deihimfard, R., Rahimi-Moghaddam, S., Collins, B., & Azizi, K. 2022. Future climate change could reduce irrigated and rainfed wheat water footprint in arid environments. Science of the Total Environment, 807, 150991.
Dickey, D. A., & Fuller, W. A. 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 1057-1072.
Enders, W. 2015. Applied econometric time series (4th ed.). New York (US): University of Alabama.
Engle, R. F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
Engle, R. F., & Granger, C. W. 1987. Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 251-276.
Engle, R., & Granger, C. 2015. Co-integration and error correction: Representation, estimation, and testing. Applied Econometrics, 39 (3), 106-135.
Eyshi Rezaie, E., & Bannayan, M. 2012. Rainfed wheat yields under climate change in northeastern Iran. Meteorological Applications, 19 (3), 346-354.
Foroozan, Z., Grießinger, J., Pourtahmasi, K., & Bräuning, A. 2020. 501 Years of spring precipitation history for the semi-arid northern Iran derived from tree-ring δ18O data. Atmosphere, 11 (9), 889.
Gerber, J. S., Ray, D. K., Makowski, D., Butler, E. E., Mueller, N. D., West, P. C., ... & Sloat, L. 2024. Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation. Nature Food, 5 (2), 125-135.
Grassini, P., Eskridge, K. M., & Cassman, K. G. 2013. Distinguishing between yield advances and yield plateaus in historical crop production trends. Nature Communications, 4 (1), 2918.
Gobbett, D. L., Hochman, Z., Horan, H., Garcia, J. N., Grassini, P., & Cassman, K. G. 2017. Yield gap analysis of rainfed wheat demonstrates local to global relevance. The Journal of Agricultural Science, 155 (2), 282-299.
Gujarati, D. N. 2021. Essentials of econometrics. Sage Publications.
Haile, M. G., Kalkuhl, M., & von Braun, J. 2016. Worldwide acreage and yield response to international price change and volatility: a dynamic panel data analysis for wheat, rice, corn, and soybeans. American Journal of Agricultural Economics, 98 (1), 172-190.
Hazell, P. B. 1982. Instability in Indian foodgrain production (Vol. 30). Intl Food Policy Res Inst.
Hendricks, N. P., Smith, A., & Sumner, D. A. 2014. Crop supply dynamics and the illusion of partial adjustment. American Journal of Agricultural Economics, 96 (5), 1469-1491.
Hornbeck, R., & Keskin, P. 2014. The historically evolving impact of the ogallala aquifer: Agricultural adaptation to groundwater and drought. American Economic Journal: Applied Economics, 6 (1), 190-219.
Hurvich, C. M., & Tsai, C. L. 1989. Regression and time series model selection in small samples. Biometrika, 76 (2), 297-307.
Iizumi, T., & Ramankutty, N. 2015. How do weather and climate influence cropping area and intensity?. Global Food Security, 4, 46-50.
Jarque, C. M., & Bera, A. K. 1980. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6 (3), 255-259.
Jiang, H., Hu, H., Li, B., Zhang, Z., Wang, S., & Lin, T. 2021. Understanding the non-stationary relationships between corn yields and meteorology via a spatiotemporally varying coefficient model. Agricultural and Forest Meteorology, 301, 108340.
Johansen, S., & Juselius, K. 1990. Some structural hypotheses in a multivariate cointegration analysis of the purchasing power parity and the uncovered interest parity for UK (No. 90-05). University of Copenhagen.
Kalkuhl, M., & Wenz, L. 2020. The impact of climate conditions on economic production. Evidence from a global panel of regions. Journal of Environmental Economics and Management, 103, 102360.
Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. 1992. Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of Econometrics, 54 (1-3), 159-178.
Ljung, G. M., & Box, G. E. 1978. On a measure of lack of fit in time series models. Biometrika, 65 (2), 297-303.
Lobell, D. B., & Burke, M. B. 2010. On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150 (11), 1443-1452.
Lütkepohl, H. 2013. Introduction to multiple time series analysis. Springer Science & Business Media.
Lütkepohl, H., & Krätzig, M. (Eds.). 2004. Applied time series econometrics. Cambridge University Press.
Madani, K. 2014. Water management in Iran: what is causing the looming crisis?. Journal of Environmental Studies and Sciences, 4 (4), 315-328.
MacKinnon, J. G., Haug, A. A., & Michelis, L. 1999. Numerical distribution functions of likelihood ratio tests for cointegration. Journal of Applied Econometrics, 14 (5), 563-577.
Mbua, I. A., & Atta-Aidoo, J. 2023. Acreage supply response of sugarcane out-growers in Tanzania: A vector error correction model (VECM) approach. Cogent Food & Agriculture, 9 (1), 2229575.
Miao, R., Khanna, M., & Huang, H. 2016. Responsiveness of crop yield and acreage to prices and climate. American Journal of Agricultural Economics, 98 (1), 191-211.
Minhas, B. S., & Vaidyanathan, A. 1965. Growth of crop output in India, 1951-54 to 1958-61: An analysis by component elements. Journal of the Indian Society of Agricultural Statistics, 17 (2), 230-252.
Naylor, R., Falcon, W., & Zavaleta, E. 1997. Variability and growth in grain yields, 1950-94: does the record point to greater instability?. Population and Development Review, 41-58.
Perron, P. 1989. The great crash, the oil price shock, and the unit root hypothesis. Econometrica: Journal of the Econometric Society, 1361-1401.
Phillips, P. C., & Perron, P. 1988. Testing for a unit root in time series regression. Biometrika, 75 (2), 335-346.
Roustaei, B., Kazemnejad, M., Rabiei, M., & Hosseini, S. Z. 2020. Study the Impact of Guaranteed Price and Guaranteed Purchase policies on Cultivation Area and Production of Wheat. Agricultural Economics and Development, 28 (3), 229-259. [In Persian]
Sims, C. A. 1980. Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
Stock, J. H., & Watson, M. W. 2001. Vector autoregressions. Journal of Economic Perspectives, 15 (4), 101-115.
Tilman, D., Balzer, C., Hill, J., & Befort, B. L. 2011. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108 (50), 20260-20264.
Trigo, R. M., Gouveia, C. M., & Barriopedro, D. 2010. The intense 2007–2009 drought in the Fertile Crescent: Impacts and associated atmospheric circulation. Agricultural and Forest Meteorology, 150 (9), 1245-1257.
Uhlig, H. 2012. Economics and reality. Journal of Macroeconomics, 34 (1), 29-41.
Wei, D., Gephart, J. A., Iizumi, T., Ramankutty, N., & Davis, K. F. 2023. Key role of planted and harvested area fluctuations in US crop production shocks. Nature Sustainability, 6 (10), 1177-1185.
Zaveri, E., Russ, J., & Damania, R. 2020. Rainfall anomalies are a significant driver of cropland expansion. Proceedings of the National Academy of Sciences, 117 (19), 10225-10233.
Zivot, E., & Andrews, D. W. K. 2002. Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. Journal of Business & Economic Statistics, 20 (1), 25-44