An algorithmic approach for determining the optimal sowing dates for crops in Iran

Articles in Press

Document Type : scientific research article

Authors

1 2- Associate Professor, Department of Plant Production, Faculty of Agriculture, Higher Educational Complex of Saravan, Iran, P.O. Box 9951634145.

2 Aburihan Campus, University of Tehran

3 Gorgan University of Agricultural Sciences and Natural Resources

4 Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources

5 Department of Agronomy. Faculty of plant production. GUASNR. Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources

6 Department of Agronomy. Faculty of Plant Production. GUASNR

7 Department of Weed Research, Iranian Plant Protection Research Institute

8 Agricultural Research, Education and Extension Organization

9 Rafsanjan University

10 دانشگاه آزاد گرگان

11 . Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources

12 Professor, Department of agronomy, Gorgan University of Agricultural Sciences and Natural Resources, Iran

Abstract

Background and Objectives: The selection of sowing dates within crop simulation models holds great importance when addressing issues pertaining to food security and climate change. Typically, statistical analyses lead to the adoption of a fixed sowing date in these models. However, it should be noted that farmers do not adhere to such a rigid schedule; rather, their sowing dates are subject to annual variations influenced by weather conditions. Consequently, incorporating climatic data becomes an effective methodology for developing algorithms and estimations regarding sowing date within crop models.

Materials and Methods: This study involved the collection of information regarding the sowing dates of 12 important crops in Iran from various provinces, with the assistance of Agricultural Research, Education and Extension Organization (AREEO) 's provincial centers. Subsequently, algorithmization was performed for each crop based on the sowing dates of farmers. The SSM-iCrop2 model was utilized to evaluate different threshold values for each algorithm in each crop, and the appropriate value was selected to ensure that the predicted sowing date aligned with that of the farmers. To evaluate the sowing algorithm, observed sowing date data were collected from various studies. For those studies where observed sowing dates were available, algorithmization of the sowing date was conducted.

Results: The results of the evaluation of different algorithms indicate that the third algorithm is well-suited for autumn crops, including wheat, barley, rapeseed, chickpeas, lentils, potatoes, and sugar beets, with sowing recommended when the average air temperature is below 16°C. Moreover, this temperature threshold increases to 17-20°C in warmer areas. Algorithm number two was found to be suitable for spring cultivation of crops such as beans, chickpeas, lentils, and potatoes, with sowing recommended when the average air temperature exceeds 7°C. For spring sugar beets, this temperature was 12°C, and for corn in cold climates and summer crops such as beans during early sowing dates, the recommended temperature range was 15-17°C.

Conclusion: The results of our study can be utilized in crop simulFor spring sugar beets, this temperature was 12°C, and for corn in cold climates and summer crops such as beans during early sowing dates, the recommended temperature range was 15-17°C.ation models to replicate farmers' sowing behavior. Additionally, these algorithms can be applied in regions where information regarding sowing dates is unavailable. By incorporating an algorithm instead of a fixed sowing date within the model, a sowing date that more closely aligns with that of the farmer can be simulated, particularly in situations where various regions and years are being considered.



Keywords: Crops, Simulation, SSM-iCrop2,Temperature.

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Journal of Plant Production Research

Articles in Press, Accepted Manuscript
Available Online from 21 April 2024

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