Effect of Climate and Management Factors on Potential and Gap of Wheat Yield in Iran with Using WOFOST Model

پژوهشهای زراعی ایران

Article 1, Volume 15, Issue 2 - Serial Number 46, July 2017, Pages 244-256   

Document Type: Research Article

DOI: 10.22067/gsc.v15i2.54278

Authors

A Koocheki^{* 1}; M Nasiri mahalati¹; H Mansouri; R Moradi²

¹Ferodwsi University of Mashhad

²Shahid Bahonar University of Kerman

Abstract

Introduction Human diets strongly rely on wheat (Triticum aestivum L.). Its production has increased dramatically during the past 50 years, partly due to area extension and new varieties but mainly as a consequence of intensified land management and introduction of new technologies. For the future, a continuous strong increase in the demand for agricultural products is expected. It is highly unlikely that this increasing demand will be satisfied by area expansion because productive land is scarce and also increasingly demanded by non-agricultural uses. The role of agricultural intensification as key to increasing actual crop yields and food supply has been discussed in several studies. However, in many regions, increases in grain yields have been declining Inefficient management of agricultural land may cause deviations of actual from potential crop yields: the yield gap. At the global scale little information is available on the spatial distribution of agricultural yield gaps and the potential for agricultural intensification.
Actual yield is mostly lower than potential yield due to inefficient management and technological that difference between these yields is considered as yield gap. Understanding of relative share of every management factors in yield gap could be as one of the important keys to reduce gap and close actual yield to potential yield.
Materials and Methods In order to evaluate the amount of wheat yield gap and also relative share of management and technological variables in yield gap, frontier production function was used which is a multi-variable regression. The frontier production function to be estimated is a Cobb-Douglas function as proposed by Coelli et al. (2005). Cobb-Douglas functions are extensively used in agricultural production studies to explain returns to scale. We propose a methodology to explain the spatial variation of the potential for intensification and identifying the nature of the constraints for further intensification. We estimated a stochastic frontier production function to calculate global datasets of maximum attainable grain yields, yield gaps, and efficiencies of grain production at. Applying a stochastic frontier production function facilitates estimating the yield gap based on the actual grain yield data only, instead of using actual and potential grain yield data from different sources. Therefore, the method allows for a robust and consistent analysis of the yield gap. The factors determining the yield gap are quantified at both global and regional scales.
For this purpose, climatic information and wheat yield of different provinces were obtained from Iran meteorological organization and Agriculture Jahade organization, respectively. Wheat potential yield in different provinces was simulated by WOFOST model. Wheat gap was gained by difference between actual and potential yield in different provinces. Relative share of climatic variables in potential yield and also relative share of management variables included irrigation, fertilizer application, mechanization, pesticide application and manure in wheat yield gap was calculated by frontier production function.
Results and Discussion The results showed that the effect of precipitation and radiation on wheat potential yield was positive and the impact of temperature was negative. Precipitation had the highest impact on wheat potential yield among other climatic variables. The range of wheat yield gap was from 1646 to 4470 kg ha-1 and 29 to 58% in Iran. Generally, the effect of all management variables on wheat yield gap was negative so that wheat yield gap was reduced by improving of these variables. Among studied management variables, irrigation had the highest effect on yield gap reduction, especially in dry-warm climate and fertilizer application was the second factor which had high effect on yield gap reduction. Therefore, to reduce wheat yield gap in Iran, irrigation management and fertilizer application should be considered.
Conclusions Between studied climate variables, the relative contribution of temperature and rainfall was higher on wheat yield potential compared with radiation in all provinces except the province of Zanjan, Golestan, Gilan and Mazandaran. The highest gap yield (4470 kg ha-1) was assigned to Ilam and Mazandaran provinces. Irrigation and fertilizer application were the more affective variables in yield gap induction.

Keywords

Frontier production function; Mechanization; Irrigation; Potential yield; simulation

References

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- Aggarval, P. K. 1994. Constraints in wheat productivity in India. In: Simulating the Effect of Climatic Factors, Genotype and Management on Productivity of Wheat in India. (eds. Aggarval, P.K., Kalra, N.) pp. 1-11. Agricultural Research Institute, New Delhi, India.

3- Aigner, D., Lovell, C. A. K., and Schmidt, P. 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics 6: 21-37.

4- Anderson, W. K. 2010. Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. Field Crops Research 116: 14-22.

5- Barbier, E. B. 2003. Agricultural expansion, resource booms and growth in Latin America: implications for long-run economic development. World Development 32: 137-157.

6- Becker, M., and Johnson, D. E. 1999. Rice yield and productivity gaps in irrigated systems of the forest zone of CoÃte d'Ivoire. Field Crops Research 60: 201-208.

7- Boogaard, H. L., van Diepen, C. A., Rötter, R. P., Cabrera, J. M. C. A., and van Laar, H. H. 1998. User’s Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 1.5. DLO-Winand Staring Centre, Wageningen, Technical Document 52.

8- Cassman, K. G. 1999. Ecological Intensification of Cereal Production Systems: Yield Potential, Soil Quality, and Precision Agriculture National Academy of Sciences colloquium ‘‘Plants and Population: Is there time?”. Arnold and Mabel Beckman Center in Irvine, CA.

9- Coelli, T., Rao, P., O’Donnell, C. J., and Battese, G. E. 2005. An Introduction to Efficiency and Productivity Analysis. Kluwer Academic Publishers, Norwell, Massachusetts.

10- DeFries, R. S., Foley, J. A., Asner, G. P. 2004. Land-use choices: balancing human needs and ecosystem function. Frontiers in Ecology and the Environment 2: 249-257.

11- FAO. 2006. Food and Agriculture Organization of the United Nations. Faostat. http://faostat.fao.org/site/408/default.aspx

12- Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., and Snyder, P. K. 2005. Global consequences of land use. Science 309: 570-574.

13- Gharineh, M. H., Bakhshandeh, A. M., Andarzian, B., and Fayezizadeh, N. 2012. Agro-climatic zonation of khouzestan province based on potential yield of irrigated wheat using WOFOST model. Agroecology 4: 255-264. (in Persian with English abstract).

14- Goudriaan, J., and Van Laar, H. H. 1993. Modelling potential crop growth processes. Kluwer Academic Press

15- Hochman, Z., Gobbett, D., Holzworth, D., McClelland, T., Rees, H. V., Marinoni, O., Garcia, J. N., and Horan, H. 2012. Quantifying yield gaps in rainfed cropping systems: A case study of wheat in Australia. Field Crops Research 136: 85-96.

16- Kalra, N., Chakraborty, D., Kumar, P. R., Jolly, M., and Sharma, P. K. 2007. An approach to bridging yield gaps, combining response to water and other resource inputs for wheat in northern India, using research trials and farmers’ fields data. Agricultural Water management 93: 54-64.

17- Kamkar, B., Koocheki, A., Nasiri, M., and Rezvani Moghaddam, P. 2007. Gap yiled Analysis of cumin in 9 regions of North Khorasan, Razavi Khorasan and Sout Khorasan provinces using modelling approches. Iranian Field Crop Researches. 5: 333-341. (in Persian with English abstract).

18- Keys, E., and McConnell, W. J. 2005. Global change and the intensification of agriculture in the tropics. Global Environmental Change 15: 320-337.

19- Koning, G. H. J., and de van Diepen, C. A. 1992. Crop production potential of rural areas within the European Communities. IV: Potential, water limited and actual crop production. Working Document 68. Netherlands Scientific Council for Government Policy, The Hague.

20- Kropff, M. J., Cassman, K. G., Peng, S., Matthews, R. B., and Setter, T. L. 1994. Quantitative understanding of yield potential. In: Cassman, K.G. (Ed.), Breaking the Yield Barrier. Proceedings of a Workshop on Rice Yield Potential in Favourable Environments. International Rice Research Institute, Los Ban os, Philippines, pp. 21–38.

21- Lu, Ch., and Fan, L. 2013. Winter wheat yield potentials and yield gaps in the North China Plain. Field Crops Research 143: 98-105.

22- MAJ (Ministry of Agriculture of the I.R. of Iran) (2010) Planning and Economics Department, Statistics Bank of Iranian Agriculture, .

23- Meeusen, W., and Broeck, J. V. D. 1977. Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review 18: 435-444.

24- 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, L. 1997. Applications of Systems Approaches at the Field Level. Springer Press.

25- Nasiri Mahallati, M. 2000. Crop growth modelling. Jahad daneshgahi Mashhad press. 274 p.

26- Nasiri Mahallati, M., and Koocheki, A. 2009. Agroclimatical zoning of wheat in Khorasan Province: evaluation of potential and gap yield. Iranian Field Crop Researches 7: 965-702. (in Persian with English abstract).

27- Nassiri Mahallati, M., Koocheki, A., Moradi, R., and Mansoori, H. 2015. Long term estimation of carbon dynamic and sequestration for Iranian agro-ecosystem. II- Sequestration and emission of carbon for common agricultural crops using ICBM model. Agroecology 7: 299-314. (in Persian with English abstract).

28- Neumann, K., Verburg, P. H., Stehfest, E., and Müller, C. 2010. The yield gap of global grain production: A spatial analysis. Agricultural Systems 103: 316-326.

29- 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.

30- Pesaran, M. H., and Schmidt, P., 1999. Handbook of Applied Econometrics. Blackwell Publishers.

31- Rezaei, E., and Soltani, A. 1998. Applied analysis of regression. Isfahan University press. 159 p.

32- Rosegrant, M. W., Paisner, M. S., Meijer, S., and Witcover, J. 2001. 2020 Global Food Outlook Trends, Alternatives, and Choices. International Food Policy Research Institute, Washington, DC.

33- Ruttan, V. W., 2002. Productivity growth in world agriculture: Sources and constraints. Journal of Economic Perspectives. 16: 161-184.

34- Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., and Polasky, S. 2002. Agricultural sustainability and intensive production practices. Nature 418: 671-677.

35- Timsina, J., Pathak, H., Humphreys, E., Godwin, D., Singh, B., Shukla, A.K., and Singh, U. 2004. Evaluation of final yield and yield gap analysis in rice using, CERES rice ver. 4.0 in northwest India. 5th International Crop Science Congress 107-115 pp.

36- Trostle, R. 2008. Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices. United States Department of Agriculture.

37- Wassenaar, T., Lagacherie, P., Legros, J. P., and Rounsevell, M. D. A. 1999. Modelling wheat yield responses to soil and climate variability at the regional scale. Climate Research 11: 209-220.

38- Zare Feyzabadi, A., Koocheki, A., and Nassiri Mahallati, M. 2006. Assessing 50 years trend of changes in cultivated area, yield and production of cereals in the country and future prediction. Iranian Field Crop Researches 4: 42-69. (in Persian with English abstract).