Yield stability evaluation of peanut lines: A comparison of an experimental versus a simulation approach

Abstract The assessment of yield stability of crop breeding lines requires data from multi-locations in order to be able to represent a sufficient number of different environments. In addition, the variation in yield of the test lines not only reflects the differential responses of the genotypes to different soil and climatic conditions, which are of primary concern to breeders, but also results from the effects of several other factors and the micro-environmental variability within the sites. Crop simulation models have the potential to assist in overcoming these difficulties. The objective of this investigation was to evaluate the potential application of the CROPGRO-Peanut model for yield stability assessment of advanced peanut lines. A group of 12 large-seeded Virginia type of peanut advanced breeding lines that was under multi-location evaluation at the regional yield trial stage in Thailand from 1998 to 1999 was used. These lines were tested in farmers’ fields and research stations in the northeastern and northern regions of Thailand during both the rainy and dry seasons of 1998–1999, totaling 10 different environments. The CROPGRO-Peanut model was used to simulate yield of the individual lines for the same environments in which they were tested. Both the observed and simulated yields were subjected to a stability analysis using a conventional regression model. The results showed that the CROPGRO-Peanut model predicted the relative mean pod yields over 10 test environments of the test peanut lines reasonably well; five out of the six highest yielding lines (top 50%) were identified by both experimentation and simulation. The model also gave estimates of the regression coefficients of individual genotype means over the site mean yields that were in good agreement with those obtained from actual testing. Some discrepancies were observed, but these were seen as providing additional information for the responses of the test genotypes to environmental factors that were not accounted for by the model. It was concluded that the CROPGRO-Peanut model could be a valuable tool for the evaluation of peanut breeding lines for yield stability.

[1]  Gerrit Hoogenboom,et al.  Simulation of Crop Growth: CROPGRO Model , 2018, Agricultural Systems modeting and Simulation.

[2]  L. Lefkovitch,et al.  Stability Analysis : Where Do We Stand ? , 2003 .

[3]  Paul Teng,et al.  Systems approaches for agricultural development , 1993, Systems Approaches for Sustainable Agricultural Development.

[4]  James W. Jones,et al.  Decision support system for agrotechnology transfer: DSSAT v3 , 1998 .

[5]  Jeffrey W. White,et al.  Evaluation of a Crop Simulation Model that Incorporates Gene Action , 1997 .

[6]  H. Gauch,et al.  Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat , 1992, Theoretical and Applied Genetics.

[7]  Jeffrey W. White,et al.  Improving Physiological Assumptions Of Simulation Models By Using Gene‐Based Approaches , 2003 .

[8]  James W. Jones,et al.  DEVELOPING GENETIC COEFFICIENTS FOR CROP SIMULATION MODELS WITH DATA FROM CROP PERFORMANCE TRIALS , 2001 .

[9]  Jeffrey W. White,et al.  From genome to crop: integration through simulation modeling , 2004 .

[10]  S. S. Hundal,et al.  Forecasting growth and yield of groundnut (Arachis hypogaea) with a dynamic simulation model ‘PNUTGRO’ under Punjab conditions , 1999, The Journal of Agricultural Science.

[11]  D. F. Cox,et al.  Statistical Procedures for Agricultural Research. , 1984 .

[12]  Jeffrey W. White,et al.  Simulating effects of genes for physiological traits in a process-oriented crop model , 1996 .

[13]  James W. Jones,et al.  Modeling Growth, Development, and Yield of Grain Legumes using Soygro, Pnutgro, and Beangro: A Review , 1992 .

[14]  R. Bruce Curry,et al.  Agricultural Systems modeting and Simulation , 1997 .

[15]  W. A. Russell,et al.  Stability Parameters for Comparing Varieties , 1966 .

[16]  James W. Jones,et al.  Evaluation of the Dry Bean Model BEANGRO V1.01 for Crop Production Research in a Tropical Environment , 1995, Experimental Agriculture.

[17]  P. N. Fox,et al.  Genotype × environment interaction and adaptation , 1993 .

[18]  S. Pararajasingham,et al.  Effects of planting date on the development and yield of spring wheat: Simulation of field data , 1996 .

[19]  Naveen Kalra,et al.  Analyzing the limitations set by climatic factors, genotype, and water and nitrogen availability on productivity of wheat II. Climatically potential yields and management strategies , 1994 .

[20]  J. White,et al.  Modeling and crop improvement , 1998 .

[21]  Graeme L. Hammer,et al.  Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments , 2000 .

[22]  Jeffrey W. White,et al.  Agronomic data: advances in documentation and protocols for exchange and use , 2001 .

[23]  K. Boote,et al.  Evaluation of the groundnut model PNUTGRO for crop response to plant population and row spacing , 1994 .

[24]  R. E. Atkins,et al.  Yield Stability of Single Crosses and Three-way Hybrids of Grain Sorghum 1 , 1974 .

[25]  Evaluation of the groundnut model PNUTGRO for crop response to water availability, sowing dates, and seasons , 1994 .

[26]  H. Meinke,et al.  A sunflower simulation model: II. Simulating production risks in a variable sub-tropical environment , 1993 .

[27]  N. O. Bosemark,et al.  Plant breeding: principles and prospects. , 1993 .

[28]  G. Hoogenboom,et al.  Understanding Options for Agricultural Production , 1998, Systems Approaches for Sustainable Agricultural Development.

[29]  Hugh G. Gauch,et al.  Statistical Analysis of a Yield Trial , 1988 .

[30]  Gerrit Hoogenboom,et al.  Determination and evaluation of genetic coefficients of peanut lines for breeding applications , 2004 .

[31]  Hugh G. Gauch,et al.  Statistical Analysis of Yield Trials with MATMODEL , 1991 .

[32]  Holger Meinke,et al.  A Peanut Simulation Model: II. Assessing Regional Production Potential , 1995 .

[33]  Robin Matthews,et al.  Applications of systems approaches in plant breeding : proceedings of the SARP applications workshop held at the International Rice Research Institute (IRRI), Los Banos, Philippines, 18 April - 6 May, 1994 , 1995 .

[34]  W. Bruening,et al.  Planting date and soybean yield: evaluation of environmental effects with a crop simulation model: SOYGRO☆ , 1992 .