Yield Monitoring for Wheat and Sugar beet in Khorasan Province: 2- Estimation of Yield Gap

Document Type : Research Article


Ferdowsi University of Mashhad


Introduction: To realize global food demand by 2050 world cereal production should be increased up to 49% compared to 2006. This level of production could be achieved by annual yield increment of 1.16%. However, the current rates are much lower. At the same time, there is a very restricted area to increase cultivated lands because of resource limitation, provided that increase in crop yields is the main option to sustain food security. Potential yield (YP) could be achieved when limiting and reducing factors are completely absent during crop growth. YP is an indicator for the yielding capacity of a given environment and management system and estimating the difference between YP and actual yield, known as yield gap, is crucial for improvement of crop production systems at regional or national scale. In this study yield gap and its temporal trend for sugar beet, irrigated and rainfed wheat are estimated over Khorasan Razavi province based on the method developed by Global Yield Gap Atlas.
Materials and Methods: Following the protocol provided by Global Yield Gap Atlas, Khorasan province was clustered into agroclimatic zones using the proposed indices (cumulative degree days above 0 ºC, aridity index and temperature seasonality) based on 10 years (1384-1393) weather data. YP of sugar beet and irrigated wheat for the study period in the climatic regions was first estimated for selected cities within each region using LINTUL model and finally the simulation results were up scaled from cities to region and from regions to the whole province. The model was cross-validated against measured data using leave-one-out (LOO) method to increase accuracy of predictions. Potential yield of rainfed wheat (YW) was estimated from frontier production function which was fitted to yield data over a wide range of annual precipitation. Yield gap (YG) of the studied crops was estimated as the difference between potential (YP) and actual yields (YA) for each region and over the 10-year period. In addition exploitable gap (YG85%=85%YP-YA) was also calculated.
Results and Discussion: The accuracy of LINTUL model for simulation of sugar beet and irrigated wheat yields was considerably increased after cross validation and the prediction error was reduced by 6.5 - 7.8%. Mean YP of irrigated wheat in the climatic region 1 (temperate, semi-dry), 2 (hot, dry) and 3 (temperate, dry) was respectively, 7248, 6478 and 7852 and for the whole province 6936 kg ha-1. Time trend of YP for irrigated wheat was not significant in 3 climatic regions however, high annual variation of YP was found over the studied period. Results indicated that up to 74% of this variation was accounted for by changes in the effective grain filling period in response to temperature. YG85% of irrigated wheat in all climatic regions was increased up to 4 t ha-1 during 1384-1388 but decreased later on so that relative gap was 0.48-0.50 of YP in 1993. Average YW of rainfed wheat in the climatic regions of the province was estimated as 2000-2800 kg ha-1 with a negative trend due to decreased precipitation, the highest negative slope in YW (59 kg ha-1 y-1) was found in the hot dry region. Rainfed wheat showed an extremely high yield gap in all climatic regions and mean relative yield gap (YG/YW) was estimated as 0.75-0.80 over the province. Mean YP of sugar beet in different climatic regions of the province was estimated from 78 to 88 t ha-1 with the lowest potential in hot-dry region. However, declining trend was found in the yield gap of sugar beet in all studied regions with the highest gap filling rate of 1.44 t ha-1 y-1 in temperate-dry region.
Conclusions: Simulated YP of sugar beet and irrigated wheat were higher in temperate-semi arid regions of the province and lower in hot-dry regions. However, cold-semi arid regions had the highest YW of rainfed wheat. When up-scaled over the province, YG85% was about 50% of YP for irrigated wheat and sugar beet and 25% for rainfed wheat. It was concluded that closing yield gap of sugar beet and irrigated wheat would be possible mainly by improving management practices however, for rainfed wheat breeding strategies should be considered as the first priority.


1. Abeledo, L. G., Savin, R., and Slafer, G. A. 2008. Wheat productivity in the Mediterranean Ebro Valley: analyzing the gap between attainable and potential yield with a simulation model. European Journal of Agronomy 28: 541-550.
2. Acreche, M. M., Briceño-Felix, G., Sanchez, J. A. M., and Slafer, G. A. 2008. Physiological bases of genetic gains in Mediterranean bread wheat yield in Spain. European Journal of Agronomy 28: 162-170.
3. Affholder, F., Poeydebat, C., Corbeels, M., Scopel, and E., Tittonell, P. 2013. The yield gap of major food crops in family agriculture in the tropics: Assessment and analysis through field surveys and modeling. Field Crops Research 143: 106-118.
4. Alexandratos, N., and Bruinsma, J. 2012. World agriculture towards 2030/2050. The 2012 Revision. ESA Working paper No. 12-03, April 2012. Food and Agriculture Organization of the United Nations: Rome.
5. Batchelor, W. D., Basso, B., and Paz, J. O. 2002. Examples of strategies to analyze spatial and temporal yield variability using crop models. European Journal of Agronomy 18: 141-158.
6. Bennie, A. T. P., and Hensley, M. 2001. Maximizing precipitation utilization in dryland agriculture in South Africa - a review. Journal of Hydrology 241: 124-139.
7. Brisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F.-X., and Huard, F. 2010. Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Research 119: 201-212.
8. Bruinsma, J. 2009. The resource outlook to 2050. By how much do land; water use and crop yields need to increase by 2050? In: Proc. FAO Expert Meeting on How to Feed the World in 2050, 24–26 June 2009. FAO, Rome (available at http://www.fao.org/wsfs/forum2050/ background-documents/expert-papers/en/).
9. Casanova, D., Goudriaan, J., Bouma, J. and Epema, G. F. 1999. Yield gap analysis in relation to soil properties in direct-seeded flooded rice. Geoderma 91: 191-216.
10. Cassman, K. G. 1999. Ecological intensification of cereal production systems: Yield potential, soil quality, and precision agriculture. Proceedings of National Academy of Sciences of the U.S.A. 96: 5952-5959.
11. Cassman, K. G., Dobermann, A., Walters, D. T., and Yang, H. S. 2003. Meeting cereal demand while protecting natural resources and improving environmental quality. Annual Review of Environmental Resources 28: 315-358.
12. Cassman, K. G., Grassini, P., and van Wart, J. 2010. Crop yield potential, yield trends, and global food security in a changing climate. In: Rosenzweig, C., Hillel, D. (Eds.), Handbook of Climate Change and Agroecosystems. Imperial College Press, London, pp. 37-51.
13. De Wit, A. J. W., Boogaard, H. L., and van Diepen, C. A. 2005. Spatial resolution of precipitation and radiation: the effect on regional crop yield forecasts. Agricultural and Forest Meteorology 135: 156-168.
14. Deihimfard, R., Nassiri Mahallati, M., and Koocheki, A. 2015a. Yield gap analysis in major wheat growing areas of Khorasan province, Iran, through crop modeling. Field Crops Research 184: 28-38.
15. Deihimfard, R., Nassiri Mahallati, M., and Koocheki, A. 2015b. Simulating the potential yield and yield gaps of sugar beet due to water and nitrogen limitations in Khorasan province using SUCROS model. Journal of Agroecology 7 (3): 315-330. (in Persian with English abstract).
16. Denison, R. F. 2015. Evolutionary tradeoffs as opportunities to improve yield potential. Field Crops Research 182: 3-8.
17. Dingkuhn, M., Laza, M. R. C., Kumar, U., Mendez, K. S., Collard, B., Jagadish, K., Singh, R. K., Padolina, T., Malabayabas, M., Torres, E., Rebolledo, M. C., Manneh, B., and Sow, A. 2015. Improving yield potential of tropical rice: achieved levels and perspectives through improved ideotypes. Field Crops Research 182: 43-59.
18. Dionora, M. J. A. and Kropff, M. J. 1995. Variation in rate and duration of grain filling in rice genotypes. In: Aggarwal, P. K., et al. (eds.). Application of Systems Approaches in Plant Breeding. SARP Research Proceedings, International Rice Research Institute, Los Banos, Philippines. pp: 123-127.
19. Efron, B. 1983. Estimating the error rate of a prediction rule: Some improvements on cross validation. Journal of the American Statistical Association 78: 316-331.
20. Efron, B., and Tibshirani, R. J. 1998. An Introduction to the Bootstrap. Boca Raton, Fla.: CRC Press.
21. Elliott, J. A., and De Jong, E. 1993. Prediction of field denitrification rates: a boundary-line approach. Soil Science Society of America Journal 57: 82-87.
22. 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., Mutters, R., Wilson, L. T., and Linquist, B. A. 2016. Yield gap analysis of US rice production systems shows opportunities for improvement. Field Crops Research 196: 276-283.
23. Ewert, F., Rounsevell, M. D. A., Reginster, I., Metzger, M. J., and Leemans, R. 2005. Future scenarios of European agricultural land use I. Estimating changes in crop productivity. Agriculture, Ecosystem and Environment 107: 101-116.
24. FAOSTAT. 2013. Crop production Statistics. Food and Agriculture Organization: Rome. At www.faostat. fao.org.
25. FAOSTAT. 2015. Crop production Statistics. Food and Agriculture Organization: Rome. At www.faostat. fao.org.
26. Fischer, G. 2009. World food and agriculture to 2030/50: how do climate change and bioenergy alter the long-term outlook for food, agriculture and resource availability? In: Proc. FAO Expert Meeting on How to Feed the World in 2050, (available at http://www.fao.org/wsfs/forum2050/backgrounddocuments/ expert-papers/en/).
27. Fischer, R. A. 2015. Definitions and determination of crop yield, yield gaps, and of rates of change. Field Crops Research 182: 9-18.
28. Fischer, R. A., and Edmeades, G. O. 2010. Breeding and cereal yield progress. Crop Science 50: S86-S98.
29. Fulco, L., and Senthold, A. 2006. Climate change impacts on wheat production in a Mediterranean environment in Western Australia. Agricultural Systems: 90: 159-179.
30. Gharineh, M. H., Bakhshandeh, A., Andarzian, B., and Faieai zadeh, N. 2012. Agroecological zoning of Khozestan province for potential yield of irrigated wheat using WOFOST model. Journal of Agroecology 4 (3): 255-264. (in Persian with English abstract).
31. Grassini, P., Thornburn, J., Burr, C., and Cassman, K. G. 2011. High-yield irrigated maize in the Western US Corn Belt: I. On-farm yield, yield potential, and impact of agronomic practices. Field Crops Research 120: 144-152.
32. Grassini, P., van Bussel, L. G., van Wart, J., Wolf, J., Claessens, L., Yang, H., Boogaard, H., de Groot, H., van Ittersum, M. K., and Cassman, K. G. 2015. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Research 177: 49-63.
33. Hall, A. J., Feoli, C., Ingaramo, J., and Balzarini, M. 2013. Gaps between farmer and attainable yields across rainfed sunflower growing regions of Argentina. Field Crops Research 143: 119-129.
34. Hall, A. J., and Richards, R. A. 2013. Prognosis for genetic improvement of yield potential and water-limited yield of major grain crops. Field Crops Research 143: 18-33.
35. Hochman, Z., Gobbett, D., Holzworth, D., McClelland, T., van Reese, H., Marinoni, O., Garcia, J. N., and Horan, H. 2013. Reprint of “Quantifying yield gaps in rainfed cropping systems: A case study of wheat in Australia”. Field Crops Research 143: 65-75.
36. Hochman, Z., Gobbett, D., Horan, H., and Garcia,,J. N. 2016. Data rich yield gap analysis of wheat in Australia. Field Crops Research 197: 97-106.
37. Hoffmann, C. M., and Kluge-Severin, S. 2010. Light absorption and radiation use efficiency of autumn and spring sown sugar beets. Field Crops Research 119: 238-244.
38. Irmak, A., J. W. Jones, T. Mavromatis, S. M. Welch, K. J. Boote, and G. G. Wilkerson. 2000. Evaluating methods for simulating cultivar responses using cross validation. Agronomy Journal 92 (6): 1140-1149.
39. Jaggard, K. W., Qi, A., Eric, S., and Ober, E. S. 2010. Possible changes to arable crop yields by 2050. Philosophical Transaction of Royal Society of Biology 365: 2835-2851.
40. Jaggard, K. W., and Werker, A. R. 1999. An evaluation of the potential benefits and costs of autumn-sown sugarbeet in Europe. Journal of Agricultural Science (Cambridge) 132: 91-102.
41. Jones, P. N., and P. S. Carberry. 1994. A technique to develop and validate simulation models. Agricultural Systems 46: 427-442.
42. Kenter, C., Hoffmann, C. M., and Märländer, B. 2006. Effects of weather variables on sugar beet yield development. European Journal of Agronomy 24: 62-69.
43. Kiniry, J. R., Bean, B., Xie, Y., and Chen, P. 2004. Maize yield potential: critical processes and simulation modeling in a high-yielding environment. Agricultural Systems 82: 45-56.
44. Kluge-Severin, S., Hoffmann, C., and Märländer, B. 2009. Yield and quality of winter beets - prospects for sugarbeet production? Zuckerindustrie 134: 366-376.
45. Koning, N., and van Ittersum, M. K. 2009. Will the world have enough to eat? Current Opinions on Environmental Sustainability 1: 77-82.
46. Koocheki, A., and Nassiri Mahallati, M. 2019. Contribution of genetic and agronomic measures on yield gain of irrigated wheat in Iran for 1971-2011. Journal of Agroecology (in press). (in Persian with English abstract).
47. Koocheki, A., and Nassiri Mahallati, M. 2007. Impacts of climate change and CO2 concentration on wheat yield in Iran and adaptation strategies. Iranian Journal of Field Crops Research 6 (1): 139-153. (in Persian with English abstract).
48. Koocheki, A., and Nassiri Mahallati, M. 2016. Effects of climate change on agricultural production of Iran: II. Perdicting productivity of field crops and adaptation strategies. Iranian Journal of Field Crops Research 14(2): 1-20. (in Persian with English abstract).
49. Kumbhakar, C. S., and Tsionas, E. G. 2006. Estimation of stochastic frontier production functions with input-oriented technical efficiency. Journal of Econometrics 133: 71-96.
50. Laborte, A. G., de Bie, C. A. J. M., Smaling, E. M. A., Moya, P. F., Boling, A. A., and van Ittersum, M. K. 2012. Rice yields and yield gaps in Southeast Asia: past trends and future outlook. European Journal of Agronomy 36: 9-20.
51. Lobell, D. B., Cassman, K. G., and Field, C. B. 2009. Crop yield gaps: Their importance, magnitudes, and causes. Annual Review of Environmental Resources 34: 179-204.
52. Mackay, I., Horwell, A., Garner, J., White, J., McKee, J., and Philpott, H. 2011. Reanalyses of the historical series of UK variety trials to quantify the contributions of genetic and environmental factors to trends and variability in yield over time. Theoretical and Applied Genetics 122: 225-238.
53. Merlosa, F. A., Monzon, J. P., Mercau, J. L., Taboada, M., Andradea, F. H., Halle, A. J., Jobbagy, E., Cassman, K. G., and Grassini, P. 2015. Potential for crop production increase in Argentina through closure of existing yield gaps. Field Crops Research 184: 145-154.
54. Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Sayre, R., Trabucco, A., and Zomer, R. 2013. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Global Ecology and Biogeography 22: 630-638.
55. MJA, 2015. Agricultural Statistics Yearbook, 2014-15. vol. 1. Crops. Ministry of Jehad e Keshavrzi, 2015.
56. Mondani, F. 2012. Simulating the effect of climate change on wild oat and sunn pest damage on wheat under Mashhad weather conditions. PhD thesis, Ferdowsi University of Mashhad. (in Persian with English abstract).
57. Muchow, R. C., and Kropff, M. J. 1997. Assessing the potential yield of tropical crops: role of field experimentation and simulation. In: Kropff, M.J., Teng, P.S., Aggarwal, P.K., Bouma, J., Bouman, B.A.M., Jones, J.W., Van Laar, H.H. (Eds.), Applications of Systems Approaches at the Field Level. Vol. 2. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 101-112.
58. Nassiri Mahallati, M., and Koocheki, A. 2014. Long term evaluation of yield stability trend for cereal crops in Iran. Journal of Agroecology 6 (3): 607-621. (in Persian with English abstract).
59. Nassiri Mahallati, M., and Koocheki, A. 2019. Yield monitoring for wheat and sugar beet in Khorasan province: 1- Analysis of methods for estimating potential yield. Journal of Agroecology 16 (4): 723-741. (in Persian with English abstract).
60. Nassiri Mahallati, M., Koocheki, A., and Jahan, M. 2011. Radiation absorption and use efficiency in relay intercropping and double cropping of winter wheat and maize. Iranian Journal of Field Crops Research 8 (6): 878-890. (in Persian with English abstract).
61. Nassiri Mahallati, M., and Koocheki, A. 2009. Agroecological zoning of wheat in Khorasan provinces: Estimating yield potential and yield gap. Iranian Journal of Field Crops Research 7 (2): 695-709. (in Persian with English abstract).
62. Neumann, K., Verberg, P. H., Stehfest, E., and Muller, C. 2010. The yield gap of global grain production: a spatial analysis. Agricultural Systems 103: 316-326.
63. O’Connell, M. G., O’Leary, G. J., Whitfield, D. M., and Connor, D. J. 2004. Interception of photosynthetically active radiation and radiation use efficiency of wheat, field pea and mustard in a semi-arid environment. Field Crops Research 85: 111-124.
64. Oerke, E. C., Dehne, H. W., Schonbeck, F., and Weber, A. 1994. Crop Production and Crop Protection. Estimated Losses in Major Food and Cash Crops. Elsevier, Amsterdam, The Netherlands.
65. Ortiz, R., Sayre, K. D., Govaerts, B., Gupta, R., Subbarao, G. V., Ban, T., Hodson, D., Dixon, J. M., Ortiz-Monasterio, J. I., and Reynolds, M. 2008. Climate change: Can wheat beat the heat? Agriculture Ecosystems and Environment 126: 46-58.
66. Palosuo, T., Kersebaum, K. C., Angulo, C., Helvinka, P., Moriondo, M., and Olesen, J. E. 2011. Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop models. European Journal of Agronomy 35: 103-114.
67. Parsa, S. 2008. Modeling spatial and temporal variation of sugar beet (Beta vulgaris L.) yield in Khorasan province. PhD thesis. Ferdowsi University of Mashhad. (in Persian with English abstract).
68. Reynolds, M., Bonnett, D., Chapman, S. C., Furbank, R. T., Manes, U., Mather, D. E., and Parry, M. A. J. 2011. Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies. Journal of Experimental Botany 62: 439-452.
69. Richter, G. M., Jaggard, K. W., and Mitchell, R. A. C. 2001. Modeling radiation interception and radiation use efficiency for sugar beet under variable climatic stress. Agricultural and Forest Meteorology 109: 13-25.
70. Sadras, V. O., and Angus, J. F. 2006. Benchmarking water use efficiency of rainfed wheat crops in dry mega-environments. Australian Journal of Agricultural Research 57: 847-856.
71. Schnug, E., Heym, J., and Achwan, F. 1996. Establishing critical values for soil and plant analysis by means of the Boundary Line Development System (Bolides). Communications in Soil Science and Plant Analysis 27: 2739-2748.
72. Seyed Jalali, S. A., Sarmadian, F., and Shorafa, M. 2012. Modeling potential land productivity for winter wheat in Aghili region, Khozestan province. Journal of Soil Research (Soil and Water Sciences) 27 (4): 427-439.
73. Shatar, T. M., and McBratney, A. B. 2004. Boundary-line analysis of field-scale yield response to soil properties. Journal of Agricultural Science 142: 553-560.
74. Soltani, A., Hajjarpour, A., and Vadez, V. 2016. Analysis of chickpea yield gap and water-limited potential yield in Iran. Field Crops Research 185: 21-30.
75. Taei Semiromi, J. Ghanbari, A., Amiri, F., Ghaffari, A., Siahsar, B., and Ayoubi, Sh. 2012. Agroecological zoning of wheat in the Borujen watershed: Rianfed and irrigated wheat cropping system evaluation. Journal of Agricultural Sciences and Sustainable Production 22 (4): 1-12. (in Persian with English abstract).
76. Thorp, K. R., Batchelor, W. D., Paz, J. O., Kaleita, A. L., and DeJonge, K. C. 2007. Using cross validation to evaluate CERES-Maize yield simulation within a decision support system for precision agriculture. Transactions of the American Society of Agricultural and Biological Engineers 50 (4): 1467-1479.
77. van Bussel, L. G., Grassini, P., Van Wart, J., Wolf, J., Claessens, L., Yang, H., Boogaard, H., de Groot, H., Saito, K., Cassman, K. G., and van Ittersum, M. K. 2015. From field to atlas: upscaling of location-specific yield gap estimates. Field Crops Research 177: 98-108.
78. van Delden, A. 2001 Yielding ability and weed suppression of potato and wheat under organic nitrogen management. PhD Thesis, Wageningen University, The Netherlands, ISBN 90 5808 519_/8, pp. 197.
79. van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., and Hochman, Z. 2013. Yield gap analysis with local to global relevance-A review. Field Crops Research 143: 4-17.
80. van Laar, H. H., Goudriaan, J., and, Van Keulen, H. 1997. SUCROS97: Simulation of crop growth for potential and water-limited production situations. C.T. de Wit Graduate School for Production Ecology and Resource Conservation, Wageningen, The Netherlands, pp. 52.
81. van Wart, J., van Bussel, L. G., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H., Gerber, J., Mueller, N. D., and Claessens, L. 2013. Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research 143: 44-55.
82. Viglizzo, E. F., Pordomingo, A. J., Castro, M. G., Le´rtora, F. A., and Bernardos, J. N. 2004. Scale-dependent controls on ecological functions in agroecosystems of Argentina. Agriculture, Ecosystems and Environment 101: 39-51.
83. Werker, A. R., and Jaggard, K. W. 1998. Dependence of sugar beet yield on light interception and evapotranspiration. Agricultural and Forest Meteorology 89: 229- 240.
84. Xiao, G., Zhang, Q., Yao, Y., Zhao, G., Wanga, R., Bai, H., and Zhang, F. 2008. Impact of recent climatic change on the yield of winter wheat at low and high altitudes in semi-arid northwestern China. Agriculture, Ecosystems and Environment 127: 37-42.
85. Xiong, W., Holman, I., Conway, D., Lin, E., and Li, Y. 2008. A crop model cross calibration for use in regional climate impacts studies. Ecological Modeling 213: 365-380.
86. Zhang, X., Wang, S., Sun, H., Chen, S., Shao, L., and Liu, X. 2013. Contribution of cultivar, fertilizer and weather to yield variation of winter wheat over three decades: A case study in the North China Plain. European Journal of Agronomy 50: 52-59.
87. Zhou, Y., He, Z. H., Sui, X. X., Xia, X. C., Zhang, K., and Zhang, G. S. 2007. Genetic improve-ment of grain yield and associated traits in the Northern China winter wheat region from 1960 to 2000. Crop Science 47: 245-253.
  • Receive Date: 12 February 2017
  • Revise Date: 12 September 2017
  • Accept Date: 23 October 2017
  • First Publish Date: 21 March 2019