Estimating the Yield Gap of Irrigated Wheat)Triticum aestivum L.( in Lorestan Province, Iran, the Modeling Approach

Document Type : Research Article

Authors

• Kourosh Heidari ¹
• Mashallah Daneshvar ¹
• Sajjad Rahimi-Moghaddam ¹
• Aliheidar Nasrollahi ²

¹ Department of Production Engineering and Plant Genetic, Faculty of Agriculture, Khorramabad, Iran

² Department of Water Engineering, Faculty of Agriculture, Khorramabad, Iran

10.22067/agry.2023.81319.1148

Abstract

Introduction
Removing the gap between the yield that is currently obtained by farmers and the yield that can be obtained by using the best water, soil, and crop management practices (yield gap) is the key solution to overcoming the nutritional challenge of the growing world population. Wheat has played an important role in the economy and food security of the world over the history. Considering the importance of the wheat crop in food security and human feeding, as well as the importance of Lorestan province in producing wheat crop in Iran, the current research was carried out using the modeling method in order to estimate the yield gap of irrigated wheat agro-ecosystems of Lorestan province.
Materials and Methods
The current research was conducted in 6 locations in Lorestan province, Iran (Aleshtar, Aligudarz, Borujerd, Khorramabad, Kuhdasht, and Pol-e Dokhtar). To evaluate the APSIM-wheat model for the Chamran cultivar, some independent field experiments were used under different treatments, including planting date, irrigation, and nitrogen regimes. nRMSE, d-index, and MBE indices were used to evaluate the crop model. Then, the management, soil, and climate data of the studied locations were collected. To find the most optimal sowing date, an initial simulation experiment set was performed. After obtaining the optimal sowing date for different locations, the attainable yield (85% of the potential yield) was simulated. Finally, from the difference between attainable yield and actual yield, the total yield gap for the locations was obtained. Also, the contribution of different management practices, including nitrogen, irrigation, and other reducing and limiting factors, was calculated from the total yield gap. In the current research, OriginPro software was used for all statistical analysis and drawing of figures.
Results and Discussion
The results of the model calibration showed that the APSIM-wheat model was able to simulate the days to flowing, biomass, and grain yield accurately. The normalized root mean square error for days to flowering, biomass, and grain yield was 5.13, 5.29, and 7.87%, respectively. The model validation results of the model also showed that the model simulates the grain yield well (10.3%). In the initial simulation, the best production potential (8433 kg ha^-1) was related to the October 15 sowing date. Attainable, water-limited, and nitrogen-limited yields were equal to 7179.2, 6302.7, and 4212.5 kg ha^-1 in Lorestan province. Across different locations, the water-limited yield ranged from 1.4722 to 2.7448 kg ha^-1, followed by attainable yield (from 6537.5 to 7982.7 kg ha^-1) and nitrogen-limited (from 2.2 3850 to 4414.5 kg ha^-1). The total yield gap of irrigated wheat in Lorestan province was equal to 4177.5 kg ha^-1. The results also showed that, in general, throughout the studied locations and years, the contribution of nitrogen, water, and other reducing and limiting factors in Lorestan province was equal to 69, 22, and 10% of the total yield gap. There were many changes among different locations in terms of the total yield gap in Lorestan province so it varied from 2661.8 kg ha^-1 (Pol-e Dokhtar) to 5608.4 kg ha^-1 (Aligudarz). In terms of yield gap due to water limitation, the highest amount was related to Pol-e Dokhtar (31%), and the lowest value was related to Khorramabad city (14%). For the yield gap caused by nitrogen limitation, Aligudarz had the largest share (77%), while the lowest share of this limitation was obtained for Khorramabad (59%). The maximum share of the yield gap caused by other limiting and reducing factors was also recorded in Khorramabad (27%), while its lowest value was calculated in Aligudarz and Aleshtar (without restrictions).
Conclusion
In general, the total yield gap of irrigated wheat in agro-ecosystems of Lorestan province is equal to 59%. Also, the results revealed that the largest share of the total yield gap is related to the lack of optimal application of nitrogen management (time of nitrogen application), and if the optimal management of nitrogen fertilizer is applied, the total yield gap will be significantly reduced at all studied locations in Lorestan province.
 
 

Keywords

• APSIM-wheat model
• Calibration and validation
• Water and nitrogen management
• Yield reducing and limiting factors

---

©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

References

• Ahmed, M., Aslam, M.A., Asif, M., & Hayat, R. (2014). Use of APSIM to model nitrogen use efficiency of rain-fed wheat. International Journal of Agriculture and Biology, 16(3), 461-470.
• Amiri, S., Eyni-Nargeseh, H., Rahimi-Moghaddam, S., & Azizi, K. (2021). Water use efficiency of chickpea agro-ecosystems will be boosted by positive effects of CO₂ and using suitable genotype× environment× management under climate change conditions. Agricultural Water Management, 252, 106928. https://doi .org/10.1016 /j.agwat .2021.106928.
• (2017). Agricultural Statistics. Department of Planning and Economy. Information and Communication Center. Ministry of Agriculture Jihad, Iran. (In Persian).
• Archontoulis, S.V., Miguez, F.E., & Moore, K.J. (2014). Evaluating APSIM maize, soil water, soil nitrogen, manure, and soil temperature modules in the Midwestern United States. Agronomy Journal, 106(3), 1025–
• Baygi, Z., Saifzadeh, S., Shirani Rad, A.H., Valadabadi, S.A., & Jafarinejad, A. (2017). Investigating the effects of sowing date on growth indices and yield and yield components of spring wheat cultivars in Neyshabur. Applied Research in Field Crops, 30 (2), 1-18. (In Persian with English abstract)
• Brisson, N., Gary, C., Justes, E., Roche, R., Mary, B., Ripoche, D., Zimmer, D., Sierra, J., Bertuzzi, P., Burger, P., & Bussière, F. (2003). An overview of the crop model STICS. European Journal of Agronomy, 18 (3-4), 309-32. https://doi.org/10.1016/s1161-0301(02)00110-7.
• Chapagain, T., & Good, A. (2015). Yield and production gaps in rainfed wheat, barley, and canola in Alberta. Frontiers in Plant Science, 6, 990. https://doi.org/10.3389/fpls.2015.00990 .
• Deihimfard, R., Nassiri Mahallati, M., & Koocheki, A. 2015. Yield gap analysis in major wheat growing areas of Khorasan province, Iran, through crop modeling. Field Crops Research, 184, 28-38. https://doi.org/10.1016/j.fcr.2015.09.002 .
• Deihimfard, R. 2011. Analysis of yield gaps of wheat and sugarbeet in Khorasan province using simulation modelling. In: Ph.D. Dissertation. Ferdowsi University of Mashhad, Mashhad, Iran. 181 p. (In Persian with English abstract).
• Enayatgholizadeh, M.R., Fathi, G., & Razaz, M. (2011). Evaluation grain yield and yield component of three wheat cultivars to drought stress and different levels of nitrogen. Journal of Crop Ecophysiology, 5 (17), 1-14. (In Persian with English abstract).
• Espe, M.B., Cassman, K.G., Yang, H., Guilpart, N., Grassini, P., Van Wart, J., Anders, M., Beighley, D., Harrell, D., Linscombe, S., & McKenzie, K. (2016). Yield gap analysis of US rice production systems shows opportunities for improvement. Field Crops Research, 196, 276– https://doi.org/10.1016/j.fcr.2016.07.011
• Gaydon, D.S., Wang, E., Poulton, P.L., Ahmad, B., Ahmed, F., Akhter, S., Ali, I., Amarasingha, R.P., Chaki, A.K., Chen, C., & Choudhury, B.U. (2017). Evaluation of the APSIM model in cropping systems of Asia. Field Crops Research, 204, 52-75.
• Gireesh, S., Kumbhare, N.V., Nain, M.S., Kumar, P., & Gurung, B. (2019). Yield gap and constraints in production of major pulses in Madhya Pradesh and Maharashtra. Indian Journal of Agricultural Research, 53(1), 104-107. https://doi.org/10.18805/ijare.a-5067
• Hajjarpoor, 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, 183-201. (In Persian with English abstract).
• Hiroshi, N., Satoshi, M., & Kusuda, O. (2007). Effect of nitrogen application rate and timing on grain yield and protein content of the bread cultivar in south western Japan. Plant Production Science, 11, 151-157. https://doi.org/10.1626/pps.11.151 .
• Hochman, Z., Carberry, P.S., Robertson, M.J., Gaydon, D.S., Bell, L.W., & McIntosh, P.C. (2013). Prospects for ecological intensification of Australian agriculture. European Journal of Agronomy, 44, 109-123. https://doi.org/10.1016/j.eja.2011.11.003 .
• Hochman, Z., Gobbett, D., Holzworth, D., McClelland, T., van Rees, H., Marinoni, O., Garcia, J.N., & Horan, H. (2012). Quantifying yield gaps in rainfed cropping systems: A case study of wheat in Australia. Field Crops Research, 136, 85– https://doi.org/10.1016/j.fcr.2012.07.008 .
• Holzworth, D.P., Huth, N.I., deVoil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K., van Oosterom, E.J., Snow, V., Murphy, C., & Moore, A.D. (2014). APSIM–evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software, 62, 327-50.
• Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N., Meinke, H., Hochman, Z., & McLean, G. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18 (3), 267– https://doi.org/10.1016/s1161-0301(02)00108-9 .
• Khaleghani, J. (2008). Assessing the weed damage to wheat fields. Final report of the research project. Iranian Research Institute of Plant Protection. 101 p. (In Persian with English abstract).
• Kpongor, D.S. (2007). Spatially explicit modeling of sorghum (Sorghum bicolor) production on complex terrain of a semi-arid region in Ghana using APSIM. Ph.D. Dissertation. Universitäts-und Landesbibliothek Bonn, Germany.
• 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. https://doi.org/10.1146/annurev.environ.041008.093740 .
• Madani, A., Makarem, A.H., Vazin, , & Joudi, M. (2012). The impact of post-anthesis nitrogen and water availability on yield formation of winter wheat. Plant, Soil and Environment, 58(1), 9–14. https://doi.Org /10.17221/299/2011-pse .
• Mawalagedera, S.M.M.R., Nigussie, T., McMurray, L., Walelea, C., & Brand, J.D. (2022). Yield gap of winter pulses in South Eastern Australia. Proceedings of the 20^th Australian Agronomy Conference, 18-22 September 2022, Toowoomba, Qld, Australia.
• Mendenhall, W., Sincich, T., & Boudreau, N.S. (1996). A Second Course in Statistics: Regression Analysis (Vol. 6). Prentice Hall Upper Saddle River, New Jersey.
• Mokhtassi- Bidgoli, A. (2013). Quantifying the population dynamics of flixweed (Descurainia sophia) in bread wheat under different water and nitrogen regimes. Ph.D. Dissertation, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran, pp. 184 p. (In Persian with English abstract).
• Prescott, J.A. (1940). Evaporation from a water surface in relation to solar radiation. Transactions of the Royal Society of South Australia, 6, 114-118 .
• Rahimi-Moghaddam, S., Deihimfard, R., Azizi, K., & Roostaei, M. (2021). Characterizing spatial and temporal trends in drought patterns of rainfed wheat (Triticum aestivum) across various climatic conditions: A modelling approach. European Journal of Agronomy, 129, 126333. https://doi.org/10.1016/j.eja.2021.126333
• Rahimi-Moghaddam, S., Kambouzia, J., & Deihimfard, R. (2018). Adaptation strategies to lessen negative impact of climate change on grain maize under hot climatic conditions: A model-based assessment. Agricultural and Forest Meteorology, 253, 1-14. https://doi.org/10.1016/j.agrformet.2018.01.032 .
• Rahimi-Moghaddam, S., Kambouzia, J., & Deihimfard, R. (2019). Optimal genotype× environment× management as a strategy to increase grain maize productivity and water use efficiency in water-limited environments and rising temperature. Ecological Indicators, 107, 105570. https://doi.org/10.1016/j.ecolind.2019.105570 .
• Reddy, K.R., & Hodges, H.F. (2000). Climate change and global crop productivity. Biologia Plantarum, 43, 1-5. https://doi.org/10.1079/9780851994390.0001 .
• Sarlio-Lahteenkorva, S., & Lahelma, E. (2008). Food insecurity is associated with past and present economic disadvantage and body mass index. Journal of Food and Nutrition, 131, 2880-2884. https://doi.org /10.1093 /jn /131.11.2880.
• Saxton, K.E., & Willey, P.H. (2006). The SPAW model for agricultural field and pond hydrologic simulation. Watershed Models, 401-435. https://doi.org/10.1201/9781420037432-29 .
• Seifert, E. (2014). OriginPro 9.1: Scientific data analysis and graphing software—Software review. Journal of Chemical Information and Modeling, 54(5), 1552–1552. https://doi.org/10.1021/ci500161d .
• Shahbazi, K., & Besharati, H. (2013). Overview of agricultural soil fertility status of Iran. Land Management Journal 1: 1-15. (In Persian with English abstract).
• 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.
• van Loon, M.P., Deng, N., Grassini, P., Rattalino, E., Juan, I., 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. https://doi.org/10.1016/j.fcr.2012.09.009 .
• 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. (2013). Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research, 143, 44-55. https://doi.org/10.1016/j.fcr.2012.09.009 .
• Wallach, D., & Goffinet, B. (1987). Mean squared error of prediction in models forstudying economic and agricultural systems. Biometrics, 43 561–576. https://doi.org/10.1016/j.eja.2018.09.004 .
• Willmott, C.J. & Matsuura, K. (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30, 79-82.
  http://dx.doi.org/10.3354/cr030079
• Willmott, C.J. 1982. Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society, 63, 1309–1313. https://doi.org/10.2307/2531995 .
• Zahed, M., Soltani, A., Zeinali, E., Torabi, B., Zand, E., & Alimagham, S. (2019). Modeling of irrigated wheat yield potential and gap in Iran. Journal of Crop Production, 12(3), 35-52. (In Persian with English abstract).