Document Type : Original Article

Authors

1 PhD student in Water Resources.Researcher, Soil and Water Research Department, South Khorasan Agriculture and Natural Resources Research and Education Center, AREEO, Birjand, Iran.

2 Associate Professor of water Engineering Department, University of Birjand, & Member of Drought and Climate Change Research Group of University of Birjand, Birjand, Iran.

3 Researcher, Soil and Water Research Department, South Khorasan Agriculture and Natural Resources Research and Education Center, AREEO, Birjand, Iran.

4 MSc in Water Resources Engineering, Regional Water Company of south Khorasan, Water Resources Management Company, Birjand, Iran.

Abstract

Introduction: Climate change is one of the most significant environmental challenges of the 21st century, with profound impacts on agriculture, especially in arid and semi-arid regions such as South Khorasan Province, Iran. Saffron is a strategic and valuable product, with 90% of the world's cultivation area and 93.7% of global production of this product belonging to Iran. Saffron is a low-maintenance plant, well-suited for cultivation in the arid and semi-arid regions of the country, and it also offers high economic returns. Considering the specific climatic conditions of Iran, where water is one of the limiting factors for agricultural development, saffron is an ideal agricultural product. One of the parameters influencing agricultural crop performance is the vapor pressure deficit (VPD). VPD is not only a critical factor affecting plant physiology but also has a significant impact on the water requirements of plants However, the increasing vapor pressure deficit (VPD) caused by climate change has directly affected its yield. Vapor pressure deficit (VPD) is an index representing the difference between the actual moisture content of the air and the maximum moisture the air can hold. This index is influenced by temperature and relative humidity and can be calculated using meteorological data. This study aims to examine the long-term impacts of VPD on saffron yield and predict its performance using artificial intelligence (AI) models.
 
Materials and Methods: In this study, climatic data including Monthly data of temperature, humidity, precipitation, and vapor pressure deficit (VPD) were obtained from the JRA-55 database for the years 1958 to 2023 for four regions of South Khorasan Province (Birjand, Tabas, Esfeden, and Sarayan). These data were processed using tools in the ArcGIS environment. Saffron yield data for the years 2005 to 2023 were collected from the South Khorasan Agricultural Organization and Vapor pressure deficit was calculated using existing equations. To predict the long-term saffron yield, four artificial intelligence algorithms, including Random Forest (RF), Generalized Additive Model (GAM), Random Subspace (RSS), and M5P, were used. The models were evaluated using the cross-validation technique to avoid overfitting or underfitting. To assess the performance of the models, statistical indices such as correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and relative root mean square error (RRMSE) were used.
 
Results and Discussion: The long-term analysis of vapor pressure deficit (VPD) changes and its impact on saffron yield in the studied regions revealed a gradual increase in the annual average VPD over the past 60 years. The decadal increase in average VPD for Birjand was 60 Pa, Sarayan 100 Pa, Qaen 40 Pa, and Tabas 50 Pa. The reduction in saffron yield averaged annually as 0.16 kg/ha for Birjand, 0.2 kg/ha for Sarayan, 0.13 kg/ha for Qaen, and 0.14 kg/ha for Tabas. Statistical indices were used to evaluate the performance of the models in both the training and testing stages. The performance of the Random Forest model was superior to the other models in both the training and testing phases [RRMSE= 0.01, RMSE= 0.09, MAE= 0.06, CC= 0.99] ,[RRMSE= 0.01, RMSE= 0.28, MAE= 0.23,CC= 0.94], The Generalized Additive Model (GAM) performed similarly to the Random Forest model during the training phase and ranked slightly lower in performance during the testing phase. The Random Subspace (RSS) model showed moderate performance in both the training and testing phases, with better performance during training compared to testing. The M5P model demonstrated poorer performance compared to the other models. These findings highlight the significant impact of increasing VPD on saffron yield due to climate change. The RF model, owing to its high accuracy and ability to handle complex relationships among variables, proved to be the best model for saffron yield prediction.
 
Conclusion: This study demonstrated that VPD, as a climate-sensitive parameter, plays a critical role in reducing saffron yield. The results indicated that all combined models are, to some extent, suitable for predicting crop yield. Among the algorithms used, the Random Forest model provided more accurate predictions due to combining the results of multiple decision trees and its ability to prevent data overfitting. This study recommends using the Random Forest model for future studies on saffron yield prediction based on vapor pressure deficit to manage water requirements.

Keywords

 Akbarpour, A., Khorashadizade, O., shahidi, A., & Ghochanian, E. (2014). Performance evaluation of artificial neural network models in estimate production of yield saffron based on climate parameters. Journal of Saffron Research,1(1), 27-35. [In Persian]. https://doi.org/10.22077/jsr.2013.431
Ahmar, S., Gill, R.A., Jung, K.H., Faheem, A., Qasim, M.U., Mubeen, M., & Zhou, W.  (2020). Conventional and molecular techniques from simple breeding to speed breeding in crop plants: recent advances and future outlook. International Journal of Molecular Sciences.  21 (7), 2590. https://doi.org/10.3390/ijms21072590
Carella, A., Massenti, R., & Bianco, R. (2023). Testing effects of vapor pressure deficit on fruit growth: a comparative approach using peach, mango, olive, orange, and loquat. Frontiers in Plant Science. 14:1294195. https://doi.org/10.3389/fpls.2023.1294195
Dai, A., Zhao, T., & Chen, J. (2018). Climate change and drought: a precipitation and evaporation perspective, Curr. Climate Change Reports. 4 (3), 301–312. https://link.springer.com/article/10.1007/s40641-018-0101-6
Ding, J., Yang, T., Zhao, Y., Liu, D., Wang, X., Yao, Y., Peng, S., Wang, T., & Piao, S., (2018). Increasingly important role of atmospheric aridityon Tibetan alpine grasslands. Geophysical Research Letters 45 (6), 2852–2859. https://doi.org/10.1002/2017GL076803
 FAO, WFP, and IFAD. (2012). The state of food insecurity in the world: economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition, food and agricultural organization of the united nations (FAO), the international fund for agricultural development (IFAD), and the world food programming (WFP), FAO, Rome, Italy
Grossiord, C., Buckley, T.N., Cernusak, L.A., Novick, K.A., Poulter, B., Siegwolf, R.T.W., Sperry, J.S., & McDowell, N.G. (2020). Plant responses to rising vapor pressure deficit. New Phytologist. 226 (6), 1550–1566. https://doi.org/10.1111/nph.16485
Hsiao, j., Swann, A., & kim, S. (2019). Maize yield under a changing climate: the hidden role of vapor pressure deficit. Agricultural and  Forest Meteorology .279, 107692. https://doi.org/10.1016/j.agrformet.2019.107692
Khozeymehnezhad, H., Farhangfar, H., Behdani, M., & Hassanpour, M. (2016). Assesmentof Saffron Farmers Knowledge on the Issues Associated with Irrigation (Case Study: Southen Khorasan). Saffron Agronomy and Technology, 4(1),41-50.  https://doi.org/10.22048/jsat.2016.11896
Khorramdel, S., Gheshm, R., Amin Ghafori,A., & Esmaielpour , B. (2014). Evaluation of soil texture and superabsorbent polymer impacts on agronomical characteristics and yield of saffron. Journal of saffron Research, 1(2),120-135. In Persian whit English Summery. https://jsr.birjand.ac.ir/article_1555.html
Konings, A.G., Williams, A.P., & Gentine, P., (2017). Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nature Geoscience 10 (4), 284–288. https://doi.org/10.1038/ngeo2903
Koozehgaran, S., Mousavi Baygi, M., Sanaeinejad, S, H., & Behdani, M.A. (2011). Study of the minimum, average and maximum temperature in South Khorasan to identify relevant areas for Saffron cultivation using GIS. Journal of Water and Soil. 25(4), 892-904. In Persian whit English Summery. https://doi.org/10.22067/jsw.v0i0.10257
Mollafilabi, A., Davari, K. & Amini Dehaghi, M. (2021). Saffron yield and quality as influenced by different irrigation methods. Scientia Agricola, 78(1), 1-7. In Persian whit English Summery. https://doi.org/10.1590/1678-992X-2019-0084
Nekouie, N., Behdani, M.A & Khashei-Siuki, A., (2014). Predicting saffron yield from meteorological data using expert system, Razavi and South Khorasan Provinces . Journal of Saffron Research.2(1),15-33. In Persian whit English Summery. https://doi.org/10.22077/jsr.2015.326
Pierce, W., Westerling, A.L., & Oyler, J., (2013). Future humidity trends over the western UnitedStates in the CMIP5 global climate models and variable infiltration capacity hydrologicalmodeling system. Hydrology and Earth System Sciences. 17, 1833–1850. https://doi.org/10.5194/hess-17-1833-2013
Qiu, R.J., & Katul, G.G., (2020). Maximizing leaf carbon gain in varying saline conditions: an optimization model with dynamic mesophyll conductance. The Plant Journal, 101 (3). 543–554. https://doi.org/10.1111/tpj.14553
Rawson, H.M., Begg, J.E., & Woodward, R.G., (1977). The effect of atmospheric humidity on photosynthesis, transpiration and water use efficiency of leaves of several plant species. Planta 134, 5–10. https://doi.org/10.1007/BF00390086
Redsma, P., Lansink, A., & Ewert, F. (2009). Economic impacts of climatic variability and subsidies on european agriculture and observed adaptition strategies. Journal of Mitigation and Adaptation Strategies for Global Change, 14. 35-59. http://dx.doi.org/10.1007/s11027-008-9149-2
Sellin, A., Taneda, H., & Alber, M., (2019). Leaf structural and hydraulic adjustment withrespect to air humidity and canopy position in silver birch (Betula pendula). Journal of Plant Research, 132, 369–381. https://doi.org/10.1007/s10265-019-01106-w
Skurichina, M., & Duin, R.P., (2002). Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis & Applications 5 (2), 121–135. http://dx.doi.org/10.1007/s100440200011
van Wijngaarden, W.A., & Vincent, L.A., (2004). Trends in relative humidity in Canada from1953–2003. Bull. Am. Meteorological Service of Canada . 4633–4636.
Wada, Y., & Bierkens, M.F.P., (2014). Sustainability of global water use: pastreconstruction and future projections. Environmental Research Letters, 9(10): 104003. http://dx.doi.org/10.1088/1748-9326/9/10/104003
Wang, J., Raza, A., Hu, Y., Buttar, N.A., Shoaib, M., Saber, K., & Ray, R.L. (2022). Development of monthly reference evapotranspiration machine learning models and mapping of Pakistan a comparative study. Water. 14 (10), 1666. https://doi.org/10.3390/w14101666
Yuan, W., Zheng, Y., Piao, S., Ciais, P., Lombardozzi, D., Wang, Y., & Yang., S. (2019). Increased atmospheric vapor pressure deficit reduces global vegetation growth. Science Advances., 5(8). https://doi.org/10.1126/sciadv.aax1396
Zhang, D., Du, Q., Zhang, Z., Jiao, X., Song, X., & Li, J. (2017). Vapour pressure deficit control in relation to water transport and water productivity in greenhouse tomato production during summer. Scientific Reports-Nature, 7(1), 43461. http://dx.doi.org/10.1038/srep43461