Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-25T06:17:23.208Z Has data issue: false hasContentIssue false
  • English
  • Français

Determining optimum planting dates for pearl millet for two contrasting environments using a modelling approach

Part of: AGS Millet Collection

Published online by Cambridge University Press:  14 January 2008

C. M. T. SOLER ,
N. MAMAN ,
X. ZHANG ,
S. C. MASON  and
G. HOOGENBOOM
C. M. T. SOLER*
Affiliation:
Department of Biological and Agricultural Engineering, The University of Georgia, 1109 Experiment Street, Griffin, GA 30223-1797, USA
N. MAMAN
Affiliation:
Institut National de Recherches Agronomiques du Niger, B.P. 429, Niamey, Niger
X. ZHANG
Affiliation:
Department of Biological and Agricultural Engineering, The University of Georgia, 1109 Experiment Street, Griffin, GA 30223-1797, USA Institute of Geographic Science and Natural Resources, Chinese Academy of Sciences, P.O. Box 9717, Beijing 100101, P.R. China
S. C. MASON
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583-0915, USA
G. HOOGENBOOM
Affiliation:
Department of Biological and Agricultural Engineering, The University of Georgia, 1109 Experiment Street, Griffin, GA 30223-1797, USA
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Pearl millet [Pennisetum glaucum (L) R. Br.] is an important cereal crop in Niger, West Africa and a potential crop for the United States of America (USA). Only a few studies have been conducted in either country to identify the optimum planting dates for high and stable yields, in part because planting date experiments are resource-intensive. Crop simulation models can be an alternative research tool for determining optimum planting dates and other management practices. The objectives of the present study were to evaluate the performance of the Cropping System Simulation Model (CSM)–CERES-Millet model for two contrasting environments, including Mead, Nebraska, USA and Kollo, Niger, West Africa and to use the model for determining the optimum planting dates for these two environments. Field experiments were conducted in both environments to study the impact of nitrogen fertilizer on grain yield of three varieties in Kollo and three hybrids in Mead and their associated growth and development characteristics. The CSM–CERES-Millet model was able to accurately simulate growth, development and yield for millet grown in these two contrasting environments and under different management practices that included several genotypes and different nitrogen fertilizer application rates. For Kollo, the optimum planting date to obtain the maximum yield was between 13 and 23 May for variety Heini Kirei, while for the other varieties the planting dates were between 23 May and 2 June. For Mead, the planting date analysis showed that the highest simulated yield was obtained, on average, between 19 and 29 June for hybrid 59022A×89-083 and 1361M×6Rm. Further studies should focus on evaluation and application of the millet model for other agroclimatic regions where pearl millet is an important crop.

Type
Crops and Soils
Information
The Journal of Agricultural Science , Volume 146 , Issue 4 , August 2008 , pp. 445 - 459
Copyright
Copyright © 2008 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Amadou, M., Gandah, M., Bielders, C. L. & Van Duivenbooden, N. (1999). Optimizing soil water use in Niger: research, development, and perspectives. In Efficient Soil Water Use – The Key to Sustainable Crop Production in Dry-Area Agriculture in West Asia, and North and Sub-Saharan Africa. Proceedings of the Workshops Organized by the Optimizing Soil Water Use (OSWU) Consortium, Niamey, Niger, 26–30 April 1998 and Amman, Jordan, 9–13 May 1998 (Eds Van Duivenbooden, N., Pala, M., Studer, C. & Bielders, C. L.), pp. 143–164. Aleppo, Syria: ICARDA and ICRISAT.Google Scholar
Amato, S. V. & Forrester, R. R. (1995). Evaluation of pearl millet as a feed ingredient for broiler rations. In Proceedings of the 1st National Grain Pearl Millet Symposium, Tifton, GA. 7–18 Jan. 1995 (Ed. Teare, I. D.), pp. 125–128. Tifton, GA, USA: University of Georgia.Google Scholar
Andrews, D. J. & Bramel-Cox, P. (1994). Breeding cultivars for sustainable crop production in low-input dryland agriculture in the tropics. In International Crop Science I (Ed. Buxton, D. A.), pp. 211–222. Madison, WI, USA: CSSA.Google Scholar
Andrews, D. J., Rajewski, J. F. & Kumar, K. A. (1993). Pearl millet: a new feed grain crop. In New Crops (Eds Janick, J. & Simon, J.), pp. 198–208. New York: Wiley.Google Scholar
Andrews, D. J., Hanna, W. W., Rajewski, J. F. & Collins, V. P. (1996). Advances in grain pearl millet: Utilization and production research. In Progress in New Crops (Ed. Janick, J.), pp. 170–177. Alexandria, VA, USA: ASHS Press.Google Scholar
Andrews, D. J., Rajewski, J. F. & Mason, S. C. (1998). Grain pearl millet: a new crop being developed at UNL. Extended Visions 2, 2–4.Google Scholar
Bacci, L., Cantini, C., Pierini, F., Maracchi, G. & Reyniers, F. N. (1999). Effects of sowing date and nitrogen fertilization on growth, development and yield of a short day cultivar of millet (Pennisetum glaucum L.) in Mali. European Journal of Agronomy 10, 9–21.Google Scholar
Bationo, A., Christianson, C. B. & Baethgen, W. E. (1990). Plant density and nitrogen fertilizer effects on pearl millet production in Niger. Agronomy Journal 82, 290–295.Google Scholar
Bationo, A. & Ntare, B. R. (2000). Rotation and nitrogen fertilizer effects on pearl millet, cowpea and groundnut yield and soil chemical properties in a sandy soil in the semi-arid tropics, West Africa. Journal of Agricultural Science, Cambridge 134, 277–284.Google Scholar
Bley, J. & Van Der Ploeg, R. R. (1991). A risk-probability map for millet production in southwest Niger. In Soil Water Balance in the Sudano-Sahelian Zone (Eds Sivakumar, M. V. K., Wallace, J. F., Renard, C. & Girou, C.), pp. 571–581. Oxon, UK: Institute of Hydrology.Google Scholar
Bonhomme, R., Derieux, M. & Edmeades, G. O. (1994). Flowering of diverse maize cultivars in relation to temperature and photoperiod in multilocational field trials. Crop Science 34, 156–164.Google Scholar
Brück, H., Payne, W. A. & Sattelmacher, B. (2000). Effects of phosphorus and water supply on yield, transpirational water-use efficiency, and carbon isotope discrimination of pearl millet. Crop Science 40, 120–125.Google Scholar
Cothren, J. T., Matocha, J. E. & Clark, L. E. (2000). Integrated crop management for sorghum. In Sorghum: Origin, History, Technology, and Production (Eds Smith, W. C. & Frederiksen, R. A.), pp. 409–441. New York: John Wiley & Sons.Google Scholar
Curtis, D. L. (1968). The relation between yield and date of heading in Nigerian sorghums. Experimental Agriculture 4, 93–101.Google Scholar
FAOSTAT data. (2005). Available online at http://faostat.fao.org/site/340/DesktopDefault.aspx?PageID=340 (verified 13/11/2007).Google Scholar
Fechter, J., Allison, B. E., Sivakumar, M. V. K., Verndplog, R. R. & Bley, J. (1991). An evaluation of the SWATRER and CERES millet model for south-west Niger. In Soil Water Balance in the Sudano-Sahelian Zone (Eds Sivakumar, M. V. K., Wallace, J. F., Renard, C. & Girou, C.), pp. 505–513. Oxon, UK: Institute of Hydrology.Google Scholar
Graef, F. & Haigis, J. (2001). Spatial and temporal rainfall variability in the Sahel and its effects on farmers' management strategies. Journal of Arid Environments 48, 221–231.Google Scholar
Hanna, W. W. (1995). Breeding pearl millet for grain production. In Proceedings of the National Grain Pearl Millet Symposium, Tifton, GA. 17–18 Jan. 1995 (Ed. Teare, I. D.), pp. 8–12.Google Scholar
Hoogenboom, G., Jones, J. W. & Boote, K. J. (1992). Modeling growth, development, and yield of grain legumes using SOYGRO, PNUTGRO, and BEANGRO: a review. Transaction of the ASAE 35, 2043–2055.Google Scholar
Hoogenboom, G., Jones, J. W., Wilkens, P. W., Porter, C. H., Batchelor, W. D., Hunt, L. A., Boote, K. J., Singh, U., Uryaseva, O., Bowen, W. T., Gijsman, A. J., Du Toit, A. S., White, J. W. & Tsuji, G. Y. (2004). Decision Support Systems for Agrotechnology Transfer Version 4.0. [CD-ROM]. Honolulu, HI: University of Hawaii.Google Scholar
Hunt, L. A., Parajasingham, S., Jones, J. W., Hoogenboom, G., Imamura, D. T. & Ogoshi, R. M. (1993). Gencalc: software to facilitate the use of crop models for analyzing field experiments. Agronomy Journal 85, 1090–1094.Google Scholar
Jones, J. W., Mishoe, J. W. & Boote, K. J. (1987). Introduction to Simulation and Modeling. Technical Bulletin 10. Florida, USA: ASPAC-Food and Fertilizer Technology Center.Google Scholar
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J. & Ritchie, J. T. (2003). DSSAT cropping system model. European Journal of Agronomy 18, 235–265.Google Scholar
Kathju, S., Burman, U. & Garg, B. K. (2001). Influence of nitrogen fertilization on water relations, photosynthesis, carbohydrate and nitrogen metabolism of diverse pearl millet genotypes under arid conditions. Journal of Agricultural Science, Cambridge 137, 307–318.Google Scholar
Loague, K. & Green, R. E. (1991). Statistical and graphical methods for evaluating solute transport models: overview and application. Journal of Contaminant Hydrology 7, 51–73.Google Scholar
Maman, N., Mason, S. C., Galusha, T. & Clegg, M. D. (1999). Hybrid and nitrogen influence on pearl millet production in Nebraska: yield, growth and nitrogen uptake, and nitrogen use efficiency. Agronomy Journal 91, 737–743.Google Scholar
Maman, N., Mason, S. C. & Sirifi, S. (2000). Variety and management level influence on pearl millet production in Niger: I. Grain yield and dry matter accumulation. African Crop Science Journal 8, 25–34.Google Scholar
Mavromatis, T., Boote, K. J., Jones, J. W., Irmak, A., Shinde, D. & Hoogenboom, G. (2001). Developing genetic coefficients for crop simulation models with data from crop performance trials. Crop Science 41, 40–51.Google Scholar
M'Khaitir, Y. O. & Vanderlip, R. L. (1992). Grain sorghum and pearl millet response to date and rate of planting. Agronomy Journal 84, 579–582.Google Scholar
Ong, C. K. & Monteith, J. L. (1985). Response of pearl millet to light and temperature. Field Crops Research 11, 141–160.Google Scholar
Pale, S., Mason, S. & Galusha, T. D. (2003). Planting time for early-season pearl millet and grain sorghum in Nebraska. Agronomy Journal 95, 1047–1053.Google Scholar
Payne, W. A. (2000). Optimizing crop water use in sparse stands of pearl millet. Agronomy Journal 92, 808–814.Google Scholar
Ravelo, A. C. & Planchuelo, A. M. (1993). Calibration of a Millet Simulation Model for Niger, Final Report. Niamey, Niger: AGRHYMET Center.Google Scholar
Reddy, N., Rao, K. & Ahmed, I. (2004). Geographical patterns of diversity in pearl millet germplasm from Yemen. Genetic Resources and Crop Evolution 51, 513–517.Google Scholar
Ritchie, J. T. & Alagarswamy, G. (1989). Simulation of sorghum and pearl millet phenology. In Modeling the Growth and Development of Sorghum and Pearl Millet (Eds Virmani, S. M., Tandon, H. L. S. & Alagarswamy, G.), pp. 24–26. Research Bulletin No. 12. Patancheru, Andhra Pradesh, India: ICRISAT.Google Scholar
Ritchie, J. T., Singh, U., Godwin, D. C. & Bowen, W. T. (1998). Cereal growth, development and yield. In Understanding Options for Agricultural Production (Eds Tsuji, G. Y., Hoogenboom, G. & Thornton, P. K.), pp. 79–97. Dordrecht, The Netherlands: Kluwer Academic Publishers.Google Scholar
Rockstrom, J. & de Rouw, A. (1997). Water, nutrients and slope position in on-farm millet cultivation in the Sahel. Plant and Soil 195, 311–327.Google Scholar
Rockstrom, J., Barron, J., Brouwer, J., Galle, S. & de Rouw, A. (1999). On-farm spatial and temporal variability of soil and water in pearl millet cultivation. Soil Science Society of America Journal 63, 1308–1319.Google Scholar
Ruiz-Nogueira, B., Boote, K. J. & Sau, F. (2001). Calibration and use of CROPGRO-soybean model for improving soybean management under rainfed conditions. Agricultural Systems 68, 151–173.Google Scholar
SAS Institute. (2001). SAS System, 8th edn.Cary, NC, USA: SAS Institute.Google Scholar
Saseendran, S. A., Ma, L., Nielsen, D. C., Vigil, M. F. & Ahuja, L. R. (2005). Simulating planting date effects on corn production using RZWQM and CERES-Maize models. Agronomy Journal 97, 58–71.Google Scholar
Sivakumar, M. V. K. (1988). Predicting rainy season potential from the onset of rains in southern Sahelian and Sudanian climatic zones of West Africa. Agricultural and Forest Meteorology 42, 295–305.Google Scholar
Sivakumar, M. V. K. & Salaam, S. A. (1999). Effect of year and fertilizer on water-use efficiency of pearl millet (Pennisetum glaucum) in Niger. Journal of Agricultural Science, Cambridge 132, 139–148.Google Scholar
Thornton, P. K., Bowen, W. T., Ravelo, A. C., Wilkens, P. W., Farmer, G., Brock, J. & Brink, J. E. (1997). Estimating millet production for famine early warning: an application of crop simulation modelling using satellite and ground-based data in Burkina Faso. Agricultural and Forest Meteorology 83, 95–112.Google Scholar
Tsuji, G. Y., Jones, J. W., Hoogenboom, G., Hunt, L. A. & Thornton, P. K. (1994). Introduction. In DSSAT v3, Decision Support System for Agrotechnology Transfer, vol. 1. (Eds Tsuji, G. Y., Uehara, G. & Balas, S.), pp. 1–12. Honolulu, HI, USA: University of Hawaii.Google Scholar
Tsuji, G. Y., Uehara, G. & Balas, S. (1998). DSSAT Version 3, vol. 4. Honolulu, HI, USA: University of Hawaii.Google Scholar
Vaksmann, M., Traore, S. B. & Niangado, O. (1996). Le photopériodisme des sorghos Africains. Agriculture et Développement 9, 13–18.Google Scholar
Willmott, C. J., Ackleson, G. S., Davis, R. E., Feddema, J. J., Klink, K. M., Legates, D. R., O'Donnell, J. & Rowe, C. M. (1985). Statistics for the evaluation and comparison of models. Journal of Geophysical Research: Oceans 90, 8995–9005.Google Scholar