News

 Statistics

Number of Journals52
Number of Issues2,120
Number of Articles21,944
Article View93,922,165
PDF Download28,515,946
Jafarnodeh, S., Soltani, A., Joolaie, R., Mirkarimi, S., Zeinali, E. (2023). Estimation of the Yield of Some Rainfed Crops in the Present Irrigated Lands of Iran. , 15(2), 301-317. doi: 10.22067/agry.2022.71149.1052
Safora Jafarnodeh; Afshin Soltani; Ramtin Joolaie; Shahrzad Mirkarimi; Ebrahim Zeinali. "Estimation of the Yield of Some Rainfed Crops in the Present Irrigated Lands of Iran". , 15, 2, 2023, 301-317. doi: 10.22067/agry.2022.71149.1052
Jafarnodeh, S., Soltani, A., Joolaie, R., Mirkarimi, S., Zeinali, E. (2023). 'Estimation of the Yield of Some Rainfed Crops in the Present Irrigated Lands of Iran', , 15(2), pp. 301-317. doi: 10.22067/agry.2022.71149.1052
Jafarnodeh, S., Soltani, A., Joolaie, R., Mirkarimi, S., Zeinali, E. Estimation of the Yield of Some Rainfed Crops in the Present Irrigated Lands of Iran. , 2023; 15(2): 301-317. doi: 10.22067/agry.2022.71149.1052

Estimation of the Yield of Some Rainfed Crops in the Present Irrigated Lands of Iran

بوم شناسی کشاورزی
Article 6, Volume 15, Issue 2 - Serial Number 56, July 2023, Pages 301-317    PDF (1.86 M)
Document Type: Research Article
DOI: 10.22067/agry.2022.71149.1052
Authors
Safora Jafarnodeh1; Afshin Soltani* 1; Ramtin Joolaie1; Shahrzad Mirkarimi2; Ebrahim Zeinali1
1Department of Agronomy, Faculty of Crop Production, Gorgan University of Agricultural Sciences and Natural Resources, Iran
2Department of Economics, Faculty of Agricultural Management, Gorgan University of Agricultural Sciences and Natural Resources, Iran
Abstract
Introduction
 Occurrence of drought and reduction of rainfall in the future will limit the cultivation of irrigated crops. Thus, it is probable that a part of the present irrigated lands and orchards of Iran may be unavailable for the cultivation of irrigated crops, but it is possible to cultivate rainfed crops in these lands. However, the available potential for cultivation of rainfed crops with respect to the soil type, climate and other factors is not known. Limited water resources, on the one hand, and the growing population along with increasing the need to produce food, on the other, make it necessary to have a comprehensive, practical and accurate program. Therefore, research on this issue is essential. In this study, production potential of rainfed wheat (Triticum aestivum), barley  (Hordeum vulgare L.), chickpea (Cicer arietinum L.), lentil (Lens culinaris Medik) and canola (Brassica napus)  in irrigated lands (fields and orchards) was modeled.
Materials and Methods
  Weather stations (position and distribution), long-term weather data (15 to 30 years), HC27 soil map, crop management data plant parameters were used to determine the yield in this study using SSM-iCrop2 model. In each zone, the yield was determined and compared with the actual data. In other words, the model output were compared with the actual current rainfed yields of each province and then it was determined that whether the model precision was sufficient for this study. Other calculations (determining the average yield of provinces) and generation of maps were done using ArcGIS V.10.2. The yield obtained by farmers in these lands was considered as 50 and 70 percent of yield potential. Also, the yields were categorized into four classes of excellent, good, medium and non-suitable. This classification is based on economic- agronomic profit of crop harvest.
Results and Discussion
 The results of this study showed that the conditions of rainfed production in each province of the country is suitable/appropriate for some crops and unsuitable/inappropriate for some other. In case the yield of farmers reached 70 percent of yield potential by optimum management, all provinces will be classified into the upper average group (3, 18 and 10 provinces in excellent, good and medium groups) for wheat. For barley 30 (3, 10 and 17 provinces in excellent, good and medium groups), for chickpea 30 (3, 6 and 21 provinces in excellent, good and medium groups), for lentil 31 (13 and 18 and 10 provinces in good and medium groups) and for canola 30 (4, 5 and 21 provinces in excellent, good and medium groups) provinces will be placed in the upper average group. Based on 70 percent of yield potential of canola, barley and chickpea, only on province is placed in non-suitable group. On the other hand, in case the yield of farmers reaches 50 percent of yield potential due to improper management, for wheat 30 (2, 2 and 26 provinces in excellent, good and medium groups), for barley 28 (4 and 24 provinces in good and medium groups), for chickpea 18 (4 and 14 provinces in good and medium groups), for lentil 28 (3 and 10 provinces in good and medium groups) and for canola 25 (5 and 20 provinces in good and medium groups) will be classified into the upper average group. Based on 50 percent of yield potential of rainfed wheat, barley, canola, chickpea and lentil, 1, 3, 6, 13 and 3 provinces were placed in non-suitable group, respectively.
Conclusion
 According to the results of this study, a major part of the provinces will be placed in medium and non-suitable groups in case of improper management, and agricultural productions will not satisfy the needs of the country. Therefore, it is necessary to pay a special attention to agronomic management of rainfed crops, as the agricultural production of the country will not be acceptable unless 70 percent of yield potential is achieved.
Keywords
Attainable yield; Cereals; GYGA protocol; Modeling; Pulses
References
  1. ABFA (National water and wastewater engineering company).)2018(. Ministry of Energy Announcement about Programmable Water Supply for Different Uses in Provinces and Basins. Iran Ministry of Energy. (In Persian)
  2. Alexandrov, V.A., & Hoogenboom, G. )2000(. The impact of climate variability and change on crop yield in Bulgaria. Agricultural and Forest Meteorology, 104(4), 315-327. https://doi.org/10.1016/S0168-1923(00)00166-0
  3. Bazgeer, S., Mahey, R.K., Sidhu, S., Sharma, P.K., Sood, A., Noorian, A.M., & Kamali, G.H.) 2008(. Wheat yield prediction using remotely sensed agromet trend-based models for Hoshiarpur district of Punjab, India. Journal of Applied Sciences, 8(3), 510-515. https://doi.org/10.3923/jas.2008.510.515
  4. Fader, M., Gerten, D., Krause, M., Lucht, W., &Cramer, W.)2013(. Spatial decoupling of agricultural production and consumption: quantifying dependences of countries on food imports due to domestic land and water constraints. Environmental Research Letters, 8, 14046. DOI: 10.1088/1748-9326/8/1/014046
  5. Ghaffari, A., Ghasemi, V.R., & De Pauw, E. )2015(. Agro-Climatically Zoning of Iran by UNESCO approach. Iran's Rainfed Agriculture, 4(1), 63-95. (In Persian) https://doi.org/10.22092/idaj.2015.102248
  6. Grassini, P., van Bussel, L.G.J., van Wart, J., Wolf, J., Claessens, L., Yang, H., Boogaard, H., de Groot, H., van Ittersum, M.K., & Cassman, K.G.) 2015(. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crop Research, 177, 49-63. https://doi.org/10.1016/j.fcr.2015.03.004
  7. Hajjarpoor, A.) 2016(. Yield gap analysis of wheat in Golestan province. A Thesis of Ph.D. in Crop Ecology, Gorgan University of Agricultural Sciences and Natural Resources, Iran. 128 (In Persian)
  8. Koo, J., & Dimes, J.) 2013(. HC27 Generic Soil Profile Database. Available at: http://hdl.handle.net/1902. 1/20299 Harvard Dataverse (V2).
  9. Kamali, G.A., Molaei, P., & Behyar, M.B.) 2010(. Development of Zanjan province dry land wheat Atlas by using climatic data and GIS. Journal of Water and Soil, 24(5), 894-907. https://doi.org/10.22067/jsw.v0i0.5281
  10. Keating, B.A., Herrero, M., Carberry, P.S., Gardner, J., & Cole, M.B.) 2014(. Food wedges: Framing the global food demand and supply challenge towards 2050. Global Food Security, 3, 125-132. https://doi.org/10.1016/j.gfs.2014.08.004
  11. Lashkari, A., & Bannayan, M.) 2013(. Agrometeorological study of crop drought vulnerability and avoidance in northeast of Iran. Theoretical and Applied change for European agricultural productivity, land use and policy. European Journal of Agronomy, 16(4), 239-262. (In Persian with English Summary) doi:10.1007/s00704-012-0769-9
  12. MohammadiNasab, B.) 2010(. Trend Analysis of annual rainfall over Iran. Geography and Environmental Planning Journal, 43(3), 95-106. (In Persian with English Summary)
  13. Nasseri, A., Abbasi, F., & Akbari, M.) 2017(. Estimating agricultural water consumption by analyzing water balance. Irrigation and Drainage Structures Engineering Research, 18, 17-32. (In Persian with English Summary) https://doi.org/10.22092/aridse.2017.105338.1057
  14. Nehbandani, A., Soltani, A., Taghdisi, R., Dadrasi, A., & Alimagham, S.M.) 2020(. Assessing HC27 soil database for modeling plant production. International Journal of Plant Production, 14, 679–687.
  15. Soltani, A.)2009(. Mathematical Modeling in Field Crops. Mashhad Univsity Press, Iran, 175 (In Persian)
  16. Soltani, A., and Sinclair, T.R.)2011(. A simple model for chickpea development, growth and yield. Field Crop Research 124(2), 252-260.
  17. Soltani., A., Nehbandani, A.R., Dadresi, A., Alimagham, S.M., Zeinali, E., & Torabi, B.)2018(. Agro-Ecological Zoning (AEZ) of Iran for plant production. research report. Gorgan University of Agricultural Sciences and Natural Resources, Iran. 159 (In Persian)
  18. Soltani., A., Zand, A., Alimagham, S.M., Nehbandani, A., Barani, H., Soltani., A., Torabi, B., Zeinali, E., Mirkarimi, S., & Joolaei, R.) 2019(. Analyzing the Country's Food Security to 2050 by Modeling the Correlation between Water, Land, Food and the Environment: Perspectives and Necessary Policies. Research Report. Organization Education and Extension Research Agricultural, 130 (In Persian)
  19. Soltani, A., Alimagham, M., Nehbandani, A., Torabi, B., Zeinali, E., Zand, E., Ghassemi, S., Vadez, V., Sinclair, T.R., & Van Ittersum, M.K.) 2020a(. Modeling plant production at country level as affected by availability and productivity of land and water. In: Agricultural Systems,183, 1-10. https://doi.org/10.1016/j.agsy.2020.102859
  20. Soltani, A., Alimagham, M., Nehbandani, A., Torabi, B., Zeinali, E., Dadrasi, A., Zand, E., Ghassemi, S., Pourshirazi, S., Alasti, O., Hosseini, R.S., Zahed, M., Arabameri, R., Mohammadzadeh, Z., Rahban, S., Kamari., H., Fayazi, H., Mohammadi, S., Keramat, S., Vadez, V., Van Ittersum, M.K., & Sinclair, T.R.) 2020b(. SSM-iCrop2: A simple model for diverse crop species over large areas. Agricultural Systems 1-7. https://doi.org/10.1016/j.agsy.2020.102855
  21. van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z.)2013(. Yield gap analysis with local to global relevance-A review. Field Crops Research, 143, 4-17.
  22. WPP., 2017. United Nations, department of economic and social affairs, population division. In: World Population Prospects, Online Edition. https://population.un.org/wpp/

 

Statistics
Article View: 7,207
PDF Download: 950


Journal Management System. Powered by Sinaweb