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Simulating response of drought-tolerant maize varieties to planting dates in contrasting Agro- ecologies of Nigeria Savannas

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To evaluate the ability of the model in simulating yield of maize
under varying planting dates in contrasting environments.

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Simulating response of drought-tolerant maize varieties to planting dates in contrasting Agro- ecologies of Nigeria Savannas

  1. 1. www.iita.org I www.cgiar.org Simulating response of drought-tolerant maize varieties to planting dates in contrasting Agro- ecologies of Nigeria Savannas By AbdullahiI.Tofa1,2 ,A.Y. Kamara1, U. F. Chiezey2,B.A. Babaji2 1International Institute of Tropical Agriculture, Kano State, 2Ahmadu Bello University, P.M.B. 1045, Zaria, Kaduna State 21st Annual Symposium IARSAF 16th - 19th April, 2018
  2. 2. www.iita.org I www.cgiar.org Introduction • Maize production in Nigeria has increased nearly ten-fold between1961 and 2013 • Average yield level was less than 2.0 t/ha compared to 9.5 t/ha USA and the world average of 5.5 t/ha • The increased production is mainly due to increase in cultivated land 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 0 2000 4000 6000 8000 10000 12000 Area (000 ha) Production (000 t) 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Maizeyield(kg/ha) 0 500 1000 1500 2000 2500
  3. 3. www.iita.org I www.cgiar.org Introduction cont… • Maize yields in Nigeria are low due to a myriad of reasons including: – poor soil fertility – moisture stress – pests and diseases – inappropriate agronomic practices Resulted to a huge gap between attainable yield and what is obtained by smallholder farmers
  4. 4. www.iita.org I www.cgiar.org Opportunities to increase productivity of maize in the Nigeria Savannas  Varieties with diverse maturity class,  Striga and drought-tolerant maize varieties  Soil fertility management technologies  Good agronomic practices e.g. planting dates • Planting too early may result in crop failure due to drought and, in turn, planting too late might reduce valuable growing time and crop yield. • Optimum planting date allows crops to best utilized moisture, nutrients and solar radiation.
  5. 5. www.iita.org I www.cgiar.org Problem of deployment of maize production technologies  Reports on the performance of these technologies are largely site specific  To assess the performance of these technologies on large scale could require time and expensive experiments Dominant soils Rainfall distribution Length of growing season
  6. 6. www.iita.org I www.cgiar.org Opportunities for Use of Cropping System Models to deploy Crop technologies ➢ CSMs like CERES-Maize present opportunity for extrapolating short-duration field experimental results to other years and other locations. ➢ Simulate crop growth, development and yield for specific cultivars based on the effects of weather, soil characteristics and crop management practices. ➢ Multi-locational evaluation and assessment of the adaptation of a new cultivar to a region and climate. ➢ Support the decision making process for cropping system management and agricultural policy
  7. 7. www.iita.org I www.cgiar.org Objectives of the study • To evaluate the ability of the model in simulating yield of maize under varying planting dates in contrasting environments.
  8. 8. www.iita.org I www.cgiar.org Model Calibration and Validation Calibration • Involves the modification of some model parameters such that data simulated by the error-free model fit the observed data. • Non-compliance may arise from sampling errors as well as from incomplete knowledge of the system. Validation • Involves the confirmation that the calibrated model closely represents the real situation • The most commonly used statistics for model validation are RMSE and d-index:
  9. 9. www.iita.org I www.cgiar.org Datasets for Model Calibration & Evaluation 2 field studies were conducted during the 2015 and 2016 wet seasons at Zaria and Iburu, Nigeria to calibrate and validate CERES-maize model in DSSAT. • Selected maize varieties: SAMMAZ 15 and SAMMAZ 16. • Seasonal weather records: daily rainfall, Tmax, Tmin, SRAD from 2015 and 2016 used to create weather files. • Soil profile data for the 2 locations • Top soil analysis physical and chemical properties data. • Management practices: planting date, fertilization, weeding, etc • Experimental data: yields, TDM e.t.c.
  10. 10. www.iita.org I www.cgiar.org Cultivar Genetic coefficients ` Coefficient Description SAMMAZ 15 SAMMAZ 16 P1 (OC day-1) Thermal time from seedling emergence to the end of juvenile phase. 283.0 300.0 P2 (day) Delay in development for each hour that day- length is above 12.5 hours. 0.678 0.640 P5 (OC day-1) Thermal time from silking to time of physiological maturity. 845.8 851.1 G2 (grains ear-1) Maximum kernel number per plant. 750.3 668.1 G3 (mg day-1) Kernel growth rate during linear grain filling stage under optimum conditions. 6.54 6.58 PHINT (OC day-1) Thermal time between successive leaf tip appearance. 44.00 41.51 Cultivar Statistics Anthesis day (DAP) Physiological maturity day (DAP) Grain yield (kg /ha) Tops weight (kg /ha) Harvest index SAMMAZ 15 D-Index 0.89 0.87 0.99 0.98 0.84 RMSE 0.50 1.22 66.3 533.4 0.02 SAMMAZ 16 D-Index 0.94 0.94 0.99 0.97 0.90 RMSE 0.71 1.22 68.3 542.9 0.01 Results Statistical indicators GCs allow the model to predict differences among different cultivars when planted in the same environment vegetative, reproductive and developmental growth processes are sensitive to both temperature and photoperiod In most cases, each cultivar has a unique photothermal requirement to achieve each of the developmental stages. Cultivar specific parameters are therefore used to define the sensitivity of each cultivar to day length or night length.
  11. 11. www.iita.org I www.cgiar.org Model Evaluation Results y = 0.6197x + 1275.6 R² = 0.8837 1:1 line 0 2000 4000 6000 8000 0 2000 4000 6000 8000 Yieldatharvestmaturity(kg [dm]/ha)measured Yield at harvest maturity (kg [dm]/ha) simulated RMSE =1045 D =0.92 IBURU ▲2015 ○ 2016 y = 0.6053x + 1112.7 R² = 0.9119 1:1 line 0 2000 4000 6000 0 2000 4000 6000 Yieldatharvestmaturity(kg [dm]/ha)measured Yield at harvest maturity (kg [dm]/ha) simulatedSAMMAZ 16 RMSE =734 D =0.93 SAMMAZ 15 y = 0.582x + 3728.8 R² = 0.8378 1:1 line 0 5000 10000 15000 0 5000 10000 15000 Topsweightatmaturity(kg [dm]/ha)measured Tops weight at maturity (kg [dm]/ha) simulatedSAMMAZ 15 RMSE =2208 D =0.9 y = 0.6655x + 2747.1 R² = 0.8715 1:1 line 0 5000 10000 15000 0 5000 10000 15000 Topsweightatmaturity(kg [dm]/ha)measured Tops weight at maturity (kg [dm]/ha) simulatedSAMMAZ 16 RMSE =1511.64 D =0.94
  12. 12. www.iita.org I www.cgiar.org Model Evaluation Results y = 0.8605x + 617.17 R² = 0.9474 1:1 line 0 2000 4000 6000 0 2000 4000 6000 Yieldatharvestmaturity(kg [dm]/ha)measured Yield at harvest maturity (kg [dm]/ha) simulated RMSE =516 D =0.98 y = 0.6992x + 2372.2 R² = 0.9852 1:1 line 0 5000 10000 15000 0 5000 10000 15000 Topsweightatmaturity(kg [dm]/ha)measured Tops weight at maturity (kg [dm]/ha) simulated RMSE =1784.81 D =0.95 ZARIA ▲2015 ○2016 y = 1.037x + 669.8 R² = 0.9731 1:1 line 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Yieldatharvestmaturity(kg [dm]/ha)measured Yield at harvest maturity (kg [dm]/ha) simulated RMSE =827 D =0.94 y = 0.874x + 3277.4 R² = 0.885 1:1 line 0 4000 8000 12000 0 4000 8000 12000 Topsweightatmaturity(kg [dm]/ha)measured Tops weight at maturity (kg [dm]/ha) simulated RMSE =2685 D =0.87 SAMMAZ 15 SAMMAZ 15 SAMMAZ 16 SAMMAZ 16
  13. 13. www.iita.org I www.cgiar.org Model application to predict maize response to planting dates in three agro-ecological zones • The seasonal analysis tool of DSSAT Version 4.7 • Model runs using 25 years of historical weather (1990 – 2014) data for each location. • Soil profile data for the 3 locations • 9 Planting dates with 10 days intervals beginning on early June (10) until late August (29)
  14. 14. www.iita.org I www.cgiar.org Cumulative probability of maize grain yield in response to planting date
  15. 15. www.iita.org I www.cgiar.org Cumulative probability of maize grain yield in response to planting date
  16. 16. www.iita.org I www.cgiar.org Cumulative probability of maize grain yield in response to planting date
  17. 17. www.iita.org I www.cgiar.org Cumulative probability of maize grain yield in response to planting date
  18. 18. www.iita.org I www.cgiar.org Cumulative probability of maize grain yield in response to planting date
  19. 19. www.iita.org I www.cgiar.org Cumulative probability of maize grain yield in response to planting date
  20. 20. www.iita.org I www.cgiar.org Conclusion CERES–maize model was found to be a useful decision-support tool for maize researchers in the Savanna regions of Nigeria. ➢The model predicted that early-June, mid and late August planting decreases mean grain yield in the 3 agro-ecologies most especially in SS. ➢In SS, the best planting date was mid-June delaying planting beyond mid-June consistently decreases grain yield. ➢In NGS and SGS, the best planting windows is from late-June to mid- July. ➢Mean grain yield was generally higher in NGS for both varieties. ➢SAMMAZ 15 the drought tolerant variety generally produced higher grain yield across the three agro-ecological zones especially with delay plantings.
  21. 21. www.iita.org I www.cgiar.org Thank you!!!

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