Arora, P.P., Jeena, A.S., 2001. Genetic variability studies in chickpea. Legume Research. 25, 137-138.
Arshad, M., Bakhsh, A., Ghafoor, A., 2004. Path coefficient analysis in chickpea (Cicer arietinum L.) under rainfed conditions. Pakistan Journal of Botany. 36, 75-82.
Bowman, D., 1990. Trend analysis to improve efficiency of agronomic trials in flue-cured tobacco. Agronomy Journal. 82, 499-501.
Burgueño, J., Cadena, A., Crossa, J., Banziger, M., Gilmour, A., Cullis, B. 2000. User's guide for spatial analysis of field variety trials using ASREML. Cimmyt, Mexico.
Chauhan, R.S., 2011. Studies on genetic variability for yield and quality traits of chickpea (Cicer arietinum) grown under late sown condition. Phd Thesis. Jnkvv, Jabalpur. India.
Coombes, N., 2009. DiGGer design search tool in R. New South Wales Department of Primary Industry) Available at
http://nswdpibiom. org/austatgen/software/[Verified 29 August 2017].
Cullis, B.R., Smith, A.B., Coombes, N.E., 2006. On the design of early generation variety trials with correlated data. Journal of Agricultural, Biological, and Environmental Statistics. 11, 381-393.
Dutkowski, G.W., Costa e Silva, J., Gilmour, A. R., Wellendorf, H., Aguiar, A., 2006. Spatial analysis enhances modelling of a wide variety of traits in forest genetic trials. Canadian Journal of Forest Research. 36, 1851-1870.
Federer, W.T., 1996. Recovery of interblock, intergradient, and intervariety information for incomplete block and lattice rectangle designed experiments. BU-1315-M in the Technical Report Series of the Biometrics Unit, Cornell University, Ithaca, NY 14853.
Federer, W.T., 1995. SAS for ANOVAs and recovery of interblock, intercolumn, and intergradient information. BU-1295 in the Technical Report Series of the Biometrics Unit, Cornell University, June.
Federer, W.T., Schlottfeldt, C.S., 1954. The use of covariance to control gradients in experiments. Biometrics. 10, 282-290.
Federer, W.T., Newton, E.A., Altman, N.S., 1997. Combining standard block analyses with spatial analyses under a random effects model. Pages 373-386 Modelling Longitudinal and Spatially Correlated Data. Springer, Netherland.
Holland, J., 2006. Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Science. 46, 642-654.
Iran Meteorological Organization, 2018. Iran Meteorological Organization, Khorramabad Station. Temperature and precipitation data for year 2018. [In Persian]
Ismaili, A., Karami, F., Akbarpour, O., Rezaeinejad, A., 2016. Estimation of genotypic correlation and heritability of apricot traits, using restricted maximum likelihood in repeated measures data. Canadian Journal of Plant Science. 96, 439-447.
Kirk, H.J., Haynes, F.L., Monroe, R.J., 1980. Application of trend analysis to horticultural field trials. Journal of the American Society of Horticultural Science. 105, 189 – 193.
Kumar, J., Abbo. S., 2001. Genetics of flowering time in chickpea and its bearing on productivity in semiarid environments. Advances in Agronomy. 72, 107-138.
Kumar, S., Arora, P., Jeena, A., 2001. Genetic variability studied for quantitative traits in chickpea. Agricultural Science Digest. 21, 263-264.
Littel, R., Milliken, G., Stroup, W., Wolfinger, R., 2006. SAS system for mixed models. SAS Institute Inc, Cary, NC, USA.
Ministry of Agriculture, 2017. Agriculture - Statistical Year Book of Iran. First volume: Crops. Information and Communication Technology Center, Department of Planning and Economy, Ministry of Agriculture, Tehran. Iran. [In Persian]
Nayyar, H., Chander, K., Kumar, S., Bains, T., 2005. Glycine betaine mitigates cold stress damage in chickpea. Agronomy for Sustainable Development. 25, 381-388.
Nyquist, W.E., Baker, R., 1991. Estimation of heritability and prediction of selection response in plant populations. Critical Reviews in Plant Sciences. 10, 235-322.
Papadakis, J., 1937. Methode statistique pour des experiences sur champ, Institut d'Amelioration des Plantes a Thessaloniki (Thessalonika, Greece). Bulletin Scientifique. 23, 1–30.
Parsa M, Bagheri A., 2008. Pulses. Mashhad University Press, Mashhad, Iran. [In Persian].
Piepho, H.P., Richter, C., Williams, E., 2008. Nearest neighbour adjustment and linear variance models in plant breeding trials. Biometrical Journal. 50,164-189.
Purushothaman, R., Upadhyaya, H., Gaur, P., Gowda, C., Krishnamurthy, L., 2014. Kabuli and desi chickpeas differ in their requirement for reproductive duration. Field Crops Research. 163, 24-31.
R Development Core Team, 2011. R: A Language and Environment for Statistical Computing. R Development Core Team, Vienna, Austria, http://www.r- project.org/.
Rezvani Moghaddam, P., Sadeghi Samarjan, R., 2008. Effect of sowing dates and different irrigation regimes on morphological characteristics and grain yield of chickpea (Cicer arietinum L.). Journal of Iranian Field Crop Research. 6, 315-325. [In Persian with English summary].
Roff, D. A., 2012. Evolutionary quantitative genetics. Springer Science & Business Media, Netherland.
Sandhu, T., Gumber, R., Bhullar, B., 1991. Correlated response of grain yield and protein content in chickpea. Legume Research, 14, 45-49.
Sandhu, T.S., Gumber, R.K., Bhullar, B.S., 1991. Correlated response of grain yield and protnin content in chickpea (Cicer arietinum L.). Legume Research. 14, 45-46.
Sarker, A., Singh, M., 2015. Improving breeding efficiency through application of appropriate experimental designs and analysis models: a case of lentil (Lens culinaris Medikus subsp. culinaris) yield trials. Field Crops Research. 179, 26-34.
Serraj, R., Krishnamurthy, L. Kashiwagi, J., Kumar, J., Chandra, S., Crouch, J., 2004. Variation in root traits of chickpea (Cicer arietinum L.) grown under terminal drought. Field Crops Research. 88,115-127.
Singh, M., Chaubey, Y., Sarker, A., Sen, D., 2010. Modeling unstructured heterogeneity along with spatially correlated errors in field trials. Journal of the Indian Society of Agricultural Statistics. 64, 313-321.
Singh, M., Malhotra, R., Ceccarelli, S., Sarker, A., Grando, S., Erskine, W., 2003. Spatial variability models to improve dryland field trials. Experimental Agriculture. 39, 151-160.
Stringer, J., Smith, A.B., Cullis, B.R., 2011. Spatial analysis of agricultural field experiments. In: Hinkelmann, K., (eds.), Design and analysis of experiments: special designs and applications, Volume 3 (pp. 109-136). Hoboken, N.J: Wiley-Interscience. USA.
Tamura, R.N., Nelson, L.A., Naderman, G.C., 1988. An investigation of the validity and usefulness of trend analysis for field plot data. Agronomy Journal. 80,712-718.
Warren, J., Mendez, I., 1982. Methods for Estimating Background Variation in Field Experiments. Agronomy Journal. 74, 1004-1009.
Wilkinson, G., Eckert, S., Hancock, T., Mayo, O., 1983. Nearest neighbour (NN) analysis of field experiments. Journal of the Royal Statistical Society. Series B (Methodological). 45(2), 151-211.
Zhang, H., Pala, M., Oweis, T., Harris, H., 2000. Water use and water-use efficiency of chickpea and lentil in a Mediterranean environment. Australian Journal of Agricultural Research. 51, 295-304.