نوع مقاله : مقاله کامل علمی- پژوهشی
نویسندگان
1 دانش‌آموخته دکتری زراعت، گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران
2 دانشیار، گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران،
3 استاد، گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران،
4 دانشیار، گروه زراعت، دانشکده تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Common bean (Phaseolus vulgaris L.) due to its high nutritional value has an effective role in ensuring food security of the community. In Iran, protein supply is mainly dependent on plant products, and any action to improve the yield and production of protein-rich crops, which are headed by legumes, is of particular importance. Increasing yields by optimizing production management and eliminating yield gaps is the most appropriate way to increase crop production and improve food security. Therefore, accurate estimation of potential yield and yield gap and crop production is essential for sustainable food supply. The present study aims to estimate the yield gap and production and water productivity of bean in its main climate zones in Iran based on the Global Yield Gaps Atlas (GYGA) project at the Gorgan University of Agricultural Sciences and Natural Resources was done in 2016. In order to estimate the beans yield gap and water productivity in Iran according to GYGA protocol, first the data related to farmers' yield (Ya) and bean harvested areas and production in the country in a period of 15 years from 2001 to 2015 from the Ministry of Agriculture of Iran was prepared. Then the distribution map of beans in the country was prepared. By combining the distribution map of the bean harvested areas and the climatic zoning map of the country, the main climatic zones (DCZs) of bean production were identified. Then, reference weather stations (RWSs) were selected according to the level of each climatic zone. In order to estimate the potential yield (Yp) and water productivity (Wp) based on weather data and major soil type and agronomic management meteorological in each of the selected areas, the SSM-iCrop2 simulation model was used, which was locally calibrated and evaluated. Finally, the bean yield gap (Yg) was calculated from the difference between the potential and actual yield of each RWSs was upscaled to DCZs and country-level. The results of comparing the average of beans actual yield reported by the Ministry of Agriculture of Iran with the actual yield calculated according to the GYGA protocol for the country with RMSE, CV and r values of 84 kg ha-1, 4% and 0.96 respectively, which indicated using This protocol can estimate the average yield of bean in the country with high accuracy. The average beans actual yield in Iran during the years 2001 to 2015 varied between 1.6 and 2.3 ton ha-1. Also, the average actual yield in the main climatic zones of production was 1.9 and between 1.1 (climatic zone 4202 in Germi) to 2.3 ton ha-1 (in climatic zone 3003 in Avaj). Bean potential yield was estimated from 3.4 (in climate zone 4202 in Germi) to 5.4 ton ha-1 (in climate zone 4103 in Hamedan and Bijar) with an average of 4.5 ton ha-1. Based on results, in the main climatic zones of bean production in Iran, there is a yield gap of 1.8 to 3.5 (average 2.6) ton ha-1, equivalent to 46 to 67% (average 57%). The average water productivity potential for bean in Iran was estimated to be 0.76 kg m-3. According to the results, if the factors causing the yield gap are eliminated by optimizing the management of bean production and cultivation and bringing the yield of bean fields to attainable yield (80% of potential yield), is increasing grain yield from the current value of 1.9 to 3.6 ton ha-1 with the same harvested areas, bean production in Iran will increase from the current 222,705 to 415,822 ton, which is equivalent to 46% increase in production and is considered an important step in improving food security.
کلیدواژه‌ها [English]
6.Godfray, H.C.J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, L., Muir, J.F., Pretty, J., Robinson, S., Thomas, S.M. &Toulmin, C. (2010). Food Security: The challenge of feeding 9 billion people. Science, 327, 812–818.
9.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.
10.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.
11.Lobell, D.B., Cassman, K.G. & Field, C.B. (2009). Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources, 34,179-204.
15.Nehbandani, A., Soltani, A., Zeinali, E. & Hoseini, F. (2017a). Analyzing soybean yield constraints in Gorgan and Aliabad katul using CPA method. Journal of Agroecology, 7(1), 109-123. [In Persian]
16.Nekahi, M.Z., Soltani, A., Siahmarguee, A. & Bagherani, N. (2014). Yield gap associated with crop management in wheat (Case study: Golestan province-Bandar-gaz). Journal of Crop Production, 7(2), 135-156. [In Persian]
17.Hajjarpour, A., Soltani, A. & Torabi, B. (2015). Using boundary line analysis in yield gap studies: Case study of wheat in Gorgan. Journal of Crop Production, 8(4), 183-201. [In Persian]
18.Nehbandani, A., soltani, A., zeinali, E., hoseini, F., shahoseini, A. & mehmandoy, M. (2017b). Soybean (Glycine max L. Merr.) Yield Gap Analysis using Boundary Line Method in Gorgan and Aliabad Katul. Journal of Agroecology, 9(3), 760-776. [In Persian]
19.Torabi, B., Soltani, A., Galeshi, S., Zeinali, E. & Kazemi Korgehei, M. (2013). Ranking factors causing the wheat yield gap in Gorgan. Journal of Crop Production, 6(1), 171-189. [In Persian]
20.Soltani, A., Hajjarpour, A. & Vadez, V. (2016). Analysis of chickpea yield gap and water-limited potential yield in Iran. Field Crops Research, 185, 21-30.
21.Torabi, B., Soltani, A., Galeshi, S. & Zeinali, E. (2011). Assessment of yield gap due to nitrogen management in wheat. Australian Journal of Crop Science, 5, 879-84.
22.dadrasi, A., Torabi, B., Rahimi, A., Soltani, A. & Zeinali, E. (2021). Determination of Potato (Solanum tuberosum L.) Yield Gap in Golestan Province. Journal of Agroecology, 12(4), 613-633.
23.Van Loon, M.P., Deng, N., Grassini, P., Edreira, J.I.R., Wolde-Meskel, E., Baijukya, F., Marrou, H. & Van Ittersum, M.K. (2018). Prospect for increasing grain legume crop production in East Africa. European Journal of Agronomy, 101, 140-148.
24.Nehbandani, A., Soltani, A., Rahemi-Karizaki, A., Dadrasi, A. & Nourbakhsh, F. (2021). Determination of soybean yield gap and potential production in Iran using modeling approach and GIS. Journal of Integrative Agriculture, 20(2), 395-407.
25.Arabameri, R., Soltani, A., Zeinali, E. & Torabi, B. (2021). The amount and How to distribute of chickpea and lentil yield gap in Iran. Journal of Crops Improvement, 23(2), 221-234. [In Persian]
26.Alasti, O., Zeinali, E., Soltani, A. & Torabi, B. (2020). estimation of yield gap and the potential of rainfed barley production increase in Iran. Journal of Crop Production, 13(3), 41-60. [In Persian]
27.Alizadeh Dehkordi, P., Nehbandani, A., Hassanpour-bourkheili, S. & Kamkar, B. (2020). Yield gap analysis using remote sensing and modelling approaches: Wheat in the northwest of Iran. International Journal of Plant Production, 14(3), 443-452. [In Persian]
28.Ashraf Vaghefi, S., Mousavi, S.J., Abbaspour, K.C., Srinivasan, R. & Yang, H. (2014). Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran. hydrological processes, 28(4), 2018-2032.
29.Wesseling, J.G. & Feddes, R.A. (2006). Assessing crop water productivity from field to regional scale. Agricultural Water Management, 86(1-2), 30-39.
31.Grassini, P., van Bussel, L.G.J., Van Wart, J., Wolf, J., Claessens, L., Yanga, H., Boogaarde, H., de Groot, H., van Ittersum, M.K. & Cassman, K.G. (2015b). How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Research, 177, 49–63.
32.Van Bussel, L.G.J., Grassini, P., Van Wart, J., Wolf, J., Claessens, L., Yang, H., Boogaard, H., Groot, H.D., Saito, K., Cassman, K.G. & Van Ittersum, M.K. (2015). From field to atlas: upscaling of location-specific yield gap estimates. Field Crops Research, 177, 98–108.
35.Van Wart, J., Van Bussel, L.G., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H., Gerber, J., Mueller, N.D., Claessens, L., Van Ittersum, M.K. & Cassman, K.G. (2013). Use of agro-climatic zones to upscale simulated crop yield potential. Field Crop Research, 143, 44–55.
36.Koo, J. & Dimes, J. (2013). HC27 generic soil profile database. http://hdl.handle.net/1902.1/20299, Harvard Data verse, V4.
37.Nehbandani, A., Soltani, A., Naghab, R.T., Dadrasi, A. & Alimagham, S.M. (2020). Assessing HC27 soil database for modeling plant production. International Journal of Plant Production, 14(4), 679-687.
38.Soltani, A., Alimagham, S.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., Vadeze, V., van Ittersum, M.K. & Sinclair, T.R. (2020a). SSM-iCrop2: A simple model for diverse crop species over large areas. Agricultural Systems, 182, 102855.
)