1Department of Agronomy, Kansas State University, Manhattan, KS. USA.
2Crop Science Department, North Carolina State Univ., Raleigh, NC.
3Blackland Research Center, Texas Agric. Expt. Stn., Temple, TX.
The sorghum growth model, SORKAM, is used to illustrate the impacts a crop growth model can have in crop model development, risk assessment, crop physiology, crop phenology, and crop breeding. In some cases, e.g. risk assessment, a single function from the model was adequate to determine the probability of freeze before crop maturity. In other cases, e.g. the determination of rate and duration of grain fill, finding adequate methods of simulation provided a source-sink approach to rate of fill and a constant growing degree day summation for duration of grain fill for current hybrids. This led to identification of lines with genetically longer grain fill duration which have been shown to increase yields and seed size. Thus crop models can provide new ideas and products in a number of ways.
The SORKAM, sorghum growth model has been used to determine freezing risk for sorghum and has led to identification of sorghum lines with genetically longer grain filling duration which will substantially increase grain yield.
Risk management, crop breeding, crop phenology, crop physiology, sorghum
Crop growth models have been developed over the last 3 to 4 decades and there has been much discussion about their impact. We use the sorghum growth model, SORKAM, (Arkin et al., 1976, Rosenthal et al., 1989) to describe the impact a crop model can have on model development, risk assessment, crop phenology, and crop breeding. At the 1992 Symposium on ‘Agrometeorology of Sorghum and Millet in the Semi-Arid Tropics’(ICRISAT, 1984), Dr. John Monteith chaired the section on use of crop models. He asked each presenter to answer a series of questions about their models before making their presentations. These are condensed and paraphrased in Fig. 1. We use particularly the last question to provide examples of how SORKAM has led to a number of new ideas or new products.
Figure 1. Questions asked by Dr. Monteith to determine validity and utility of each crop model.
Products derived from SORKAM
Schaffer (1980) showed that if the duration of grain fill was measured not in days but in growing degree days, there was essentially no difference in duration of grain fill among hybrids differing in maturity or among widely different planting dates. This constant duration of grain fill was used with historical weather data to provide probabilities of the crop maturing before a freeze depending on when the crop flowered (Shroyer et al., 1987). Figure 2 shows one of the probability maps.
Figure 2. Probability of sorghum maturing before a freeze if half-bloom occurred on August 19.
Sorghum has tremendous capability to compensate in the yield components of head number, seed/head, and seed size. SORKAM (Rosenthal et al., 1989) generally did too good a job of allowing yield component compensation. Heiniger et al. (1997) developed a source/sink approach to determining how much of current plant growth would be used in filling the grain. This approach gave much more realistic grain growth curves (Fig. 3) and greatly improved the capability of SORKAM to simulate yield under a range of conditions (Fig. 4).
Figure 3. Caryopsis growth compared with the original SORKAM and the source/sink functions.
Figure 4. Comparison of simulated and actual grain yields before and after inclusion of the source/sink function.
Because sorghum has such great capability for yield component compensation, marginal stands will often produce quite good yields. In addition, because low temperatures promote tillering, replanting often places the crop under environmental conditions that are less favourable for tillering, thus limiting the compensation. Replanting may also increase the risk for freeze damage at the end of the season. SORKAM was used to simulate three hybrids of differing maturity for a range of plant populations and planting dates using 30 years of weather data for 17 locations in the state. Results were provided in a decision aid (Vanderlip et al., 1995). Figure 5 shows an example comparison of expected yields from original ‘poor stand’ vs. ‘replanted’ crops.
Figure 5. Comparison of yield probabilities for an original 15 May planting of a medium maturity hybrid which only resulted in 20,000 plants/acre (squares) and a 15 June replanting with an early hybrid at 60,000 plants/acre (pluses).
The determination that current commercial hybrids lacked variability in duration of grain fill when measured on a heat-unit basis led to the question of whether there was genetic variability for this trait. If so, and if it would lead to increased yield, why wasn’t it already in the various public and private breeding programs in the U.S. and elsewhere. Simulation of yields for hybrids with increased grain fill duration (about 100 GDD) suggested that even under moisture limiting conditions yields would be increased substantially (Barten, 2001) Over 400 lines were screened and variability in duration of grain fill was found (Fig. 6). One of the lines was released as KS115 and has been used in producing experimental hybrids. Preliminary field results suggest that indeed yields can be increased, with the increases coming primarily through increased seed size (Tuinstra et al., 2001)
Figure 6. Distribution of duration of grain fill for lines grown under short-day, nearly constant temperature conditions in Mexico. The three hybrid controls filled grain for about 35 days.
Crop growth models can provide sources of new ideas and lead to improvements in determining risk in variable environments, improve understanding and simulation of crop phenology and physiology, and suggest and test the impact of specific breeding objectives.
Arkin, G. F., R. L. Vanderlip, and J. T. Ritchie. 1976. A dynamic grain sorghum growth model. Trans. ASAE 19:622-626,630.
Barten, T. J. 2001. Validation of SORKAM using field studies and predicting the effects of long grain filling duration on grain sorghum yield. M. S. Thesis. Kansas State Univ. Manhattan, KS. 129p.
Heiniger, R. W., R. L. Vanderlip, S. W. Welch, and R. C. Muchow. 1997. Developing guidelines for replanting grain sorghum: II. Improved methods of simulating caryopsis weight and tiller number. Agron. J. 89:84-92.
ICRISAT (International Crops Research Institute for the Semi-Arid Tropics). 1984. Agrometeorology of Sorghum and Millet in the Semi-Arid Tropics: Proceedings of the International Symposium, 15-20 Nov 1982, ICRISAT Center, India. Patancheru, A.P. 502324, India: ICRISAT.
Jaiyesimi, S. T. 1979. Yield and tillering response of grain sorghum [Sorghum bicolor (L.) Moench] hybrids to planting date and density. Ph. D. Dissertation. Kansas State Univ. Manhattan, KS.
Rosenthal, W. D., R. L. Vanderlip, B. S. Jackson, and G. F. Arkin. 1989. SORKAM: A grain sorghum growth model. TAES Computer Software Documentation Ser. No. MP-1669. Tex. Agric. Exp. Stn., College Station.
Schaffer, J. A. 1980. The effect of planting date and environment on the phenology and modeling of grain sorghum [Sorghum bicolor (L.) Moench]. Ph. D. Diss. Kansas State Univ., Manhattan, KS.
Shroyer, J. P., R. L. Vanderlip, D. L. Bark, J. A. Schaffer, and T. L. Walter. 1987. Probability of sorghum maturing before a freeze. Kansas State Univ., Cooperative Ext. Ser. AF-162.
Tuinstra, M. R., D. Kriegshauser, R. L. Vanderlip, K. D. Kofoid, J. D. Hancock. 2001. Can long grain-fill duration improve yield potential and grain quality of sorghum? p. 185-195. In: Proceedings of the 56th Corn & Sorghum Research Conference, 2001. American Seed Trade Association. Chicago, IL, Dec. 5-7, 2001. Alexandria, VA. USA.
Vanderlip, R.L., R.W. Heiniger, S.W. Welch, D.L. Fjell. 1995. A decision aid for determining planting and replanting management of grain sorghum. pp. 927-937. In P.C. Robert, R.H. Rust, and W.E. Larson (Ed). Proceeding of the second international conference on site-specific management for agricultural systems.