چكيده لاتين :
Introduction
Chickpea is the most important legume in West Asia and North Africa especially under rainfed conditions (Silim et al., 1993). Chickpea yield is at low levels in major producing countries (Millan et al., 2006), indicating a need to increase crop yield via crop genetic improvement and enhanced crop management. Genetic and management constraints can be analyzed by using crop simulation models. Crop models are very useful tools to evaluate the potential yield and environment constraints, genetics and management factors (Lobell et al., 2009). The yield gap (Yg) is the difference between Yp (irrigated crops), or Yw (rainfed crops) and actual yields (Ya). Any improvement of crop management practices requires that the potential yield and its difference with actual yield be determined and ultimately evaluate the determinants of yield gap (Lobell et al., 2009). Assessment of potential yield and yield gaps can help in identifying the yield limiting factors and it helps us develop suitable strategies to improve the productivity of any crop (Naab et al., 2004). In this study, yield potential and yield gap across the major chickpea-growing regions of the Khorasan Razavi province in Iran were quantified by using the SSM-chickpea model and actual yield and its variability within farmers’ fields were evaluated. This study tries to determine the potential yield capacity and chickpea yield gap.
Materials and methods
For model parameterization, a field experiment was conducted in a randomized complete design with 4 replications in the research field of the Ferdowsi University of Mashhad. The chickpea cultivar ILC482 was used in this experiment.
The chickpea model of Soltani & Sinclair (2011) was used in this study. The simulations started from the sowing date and ended at maturity. Finally, the simulated results of LAI, aboveground biomass and seed yield were examined by the root mean square error (RMSE). RMSE was calculated (Wallach & Goffinet, 1987):
Where Oi is the observed data, Pi is the simulated data and n is the total number of observations.
The study was performed at nine regions in the Khorasan Razavi province located in the Northeast of Iran, under two water conditions, i.e. potential and water limited. Irrigated and rainfed actual yields were based on statistical data at regional level for the period of 2002–2012, which were collected from the Agricultural Jihad of the Khorasan Razavi province (Anonymous, 2012). These yields were averaged out for calculating the actual yield for each region for which simulations were carried out.
Yield gaps
Yield gaps were defined as:
YGMM= Simulated potential yield - simulated water limited yield
YGMI= Simulated potential yield - irrigated actual yield
YGMR= Simulated water limited yield - rainfed actual yield
Results and discussion
The results suggest that the Khorasan Razavi province with low actual Chickpea yields has a high probability of large yield gaps and large potentials to increase current yields. The model simulations showed that the average potential yield of Chickpea for the regions was 2251 kg.ha-1, while the water limited yield was 1026 kg.ha-1 indicating a 54% reduction in yield due to adverse soil moisture conditions. The average irrigated and rainfed actual yield were also 64% and 79% less than the simulated potential and water limited yields, respectively. Across all study locations, the potential yields were less variable than water limited and actual yields, and were correlated with solar radiation during the season (R2 = 0.63, p < 0.05). Generally, YGMI and YGMM showed an increasing trend from the North (including Neishabur, Mashhad, Quchan and Daregaz regions) to the South of this province (Torbat Jam and Gonabad). In comparison with other yield gaps, the quantity of YGMR was very low because both limited simulated water and average rainfed actual yields were low in these regions. Furthermore, YGMR was more or less unaffected by the amount of rainfall received in these regions.
References
Anonymous, 2011-2012. Annual report of 2001-2012. Agricultural Research Institute, Mashhad, Iran. (In Persian)
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.
Millan, T., Clarke, H.J., Siddique, K.H.M., Buhariwalla, H.K., Gaur, P.M., Kumar, J., Gil, J., Kahl, G., and Winter, P. 2006. Chickpea molecular breeding: new tools and concepts. Euphytica 147: 81-103.
Naab, J.B., Singh, P., Boote, K.J., Jones, J.W., and Marfo, K.O. 2004. Using the CROPGRO peanut model to quantify yield gaps of peanut in the Guinean Savanna Zone of Ghana. Agronomy Journal 96: 1231-1242.
Silim, S.N., Saxana, M.C., and Singh, K.B. 1993. Adaptation of Spring-Sown Chickpea to the Mediterranean basin .II. Factors influencing yield under drought. Field Crops Research 34:137-141.
Soltani, A., and Sinclair, T.R. 2011. A simple model for chickpea development, growth and yield. Field Crops Research 124: 252-260.
Wallach, D., and Goffinet, B. 1987. Mean squared error of prediction in models for studying ecological and agronomic systems. Biometrics 43: 561-573.
