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Understanding the response of drought-tolerant
maize varieties to nitrogen application in the...
www.iita.org I www.cgiar.org
Maize production in the Nigeria savannas
 Maize is an important food security crop in Nigeri...
www.iita.org I www.cgiar.org
Maize production in the Nigeria savannas
 Poor soil fertility and low nutrient availability ...
www.iita.org I www.cgiar.org
Status of fertilizer recommendation in Nigeria
 Fertilizer recommendations in Nigeria are pr...
www.iita.org I www.cgiar.org
 Response of maize to applied N is largely
dependent on soil type, climate conditions and
ma...
www.iita.org I www.cgiar.org
Use of Crop and cropping systems models as Decision
Support Tools for Fertilizer Recommendati...
www.iita.org I www.cgiar.org
Objectives of the study
• Calibrate and evaluate the Agricultural Production Systems Simulato...
www.iita.org I www.cgiar.org
The APSIM Model
• APSIM version 7.6 is a processed based model that has been used to
study cr...
www.iita.org I www.cgiar.org
Model Calibration
➢ Calibration
• Model calibration involves the modification of some model
p...
www.iita.org I www.cgiar.org
Model Calibration
➢ Input data for model calibration
• Daily climatic conditions
– Rain- rain...
www.iita.org I www.cgiar.org
Result of model calibration
Parameters Unit EVDT IWDC2
Estimated days from end of juvenile st...
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Result of model calibration
Location 2009 EVDT IWDC2
Flowering Maturity Flowering Maturity
Si...
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Model Validation
➢ Model validation
• This involves confirming that the calibrated model clos...
www.iita.org I www.cgiar.org
Statistical indices for model validation
www.iita.org I www.cgiar.org
Result of Model validation
Abuja-Grain yield
The model accurately predicted grain yield (Kgha...
www.iita.org I www.cgiar.org
Results of Model validation
Samaru-Zaria
The model accurately predicted grain yield (Kgha-1) ...
www.iita.org I www.cgiar.org
Model application to simulate maize yield response to N
• 2 locations
– Kano in the Sudan Sav...
www.iita.org I www.cgiar.org
Model application to simulate maize yield response to
N
• Input data for the long term simula...
www.iita.org I www.cgiar.org
Cummulative probability of maize yield response to N
Samaru- Zaria
www.iita.org I www.cgiar.org
Cumulative probability of maize yield response to N
BUK-Kano
www.iita.org I www.cgiar.org
Conclusion
• The model was reasonably calibrated and evaluated for the two
varieties with all...
www.iita.org I www.cgiar.org
Thank you for listening
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Understanding the response of drought-tolerant maize varieties to nitrogen application in the Nigeria savannas using APSIM Model

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this project seeks to calibrate and evaluate the Agricultural Production Systems Simulator(APSIM) model for simulating the response of two maize cultivars(2009 EVDT and IWDC2) to applied nitrogen.

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Understanding the response of drought-tolerant maize varieties to nitrogen application in the Nigeria savannas using APSIM Model

  1. 1. www.iita.org I www.cgiar.org Understanding the response of drought-tolerant maize varieties to nitrogen application in the Nigeria savannas using APSIM Model By Aloysius Beah1,2, A.Y. Kamara2, J.M. Jibrin1 and A.I.Tofa2 1Bayero University Kano 2IITA, Kano, Nigeria
  2. 2. www.iita.org I www.cgiar.org Maize production in the Nigeria savannas  Maize is an important food security crop in Nigeria produced on a land area of 5.6million ha.  Annual maize production is 7.2 million MT  Maize yields in Nigeria are generally low and vary significantly from one location to another due to several reasons including: – Poor soil fertility and low nutrient availability particularly Nitrogen – Variable climate (Climate varies from one location to another) – Low and often unpredictable rainfall patterns – Low use of improved inputs including seeds and N fertilizers – Pests and diseases
  3. 3. www.iita.org I www.cgiar.org Maize production in the Nigeria savannas  Poor soil fertility and low nutrient availability have been pointed out as the most serious biophysical limitations to maize production in the Nigeria Savannas.  Nitrogen is the main driving force to produce large yields because nitrogen is vitally important and it is required in large amounts. Nitrogen deficiency on maize
  4. 4. www.iita.org I www.cgiar.org Status of fertilizer recommendation in Nigeria  Fertilizer recommendations in Nigeria are presently based on agro ecologies and are uniform for all maize varieties  The published N, P, and K recommendation per hectare is 120 kg N, 60 kg phosphorus pentoxide (P2O5), and 60 kg dipotassium oxide (K2O) for all open-pollinated maize varieties across the Sahel, Sudan and Northern Guinea Savannas
  5. 5. www.iita.org I www.cgiar.org  Response of maize to applied N is largely dependent on soil type, climate conditions and maize variety  The blanket fertilizer recommendations for maize does not account for differences in soil type and site specific differences in the micro-climates  To account for soil type and weather conditions would require the establishment of several trials across the country which may be expensive and time consuming Dominantsoils Rainfall distribution Slopeclasses Limitations of blanket fertilizer recommendation
  6. 6. www.iita.org I www.cgiar.org Use of Crop and cropping systems models as Decision Support Tools for Fertilizer Recommendation  Decision support tools such as crop and cropping systems models can be used to predict the response of maize to fertilizer nutrients on different soils under variable climate conditions
  7. 7. www.iita.org I www.cgiar.org Objectives of the study • Calibrate and evaluate the Agricultural Production Systems Simulator (APSIM) model for simulating the response of two maize cultivars (2009 EVDT and IWDC2) to applied nitrogen. • Simulate the long term response of maize to applied N in two agro- ecologies in northern Nigeria
  8. 8. www.iita.org I www.cgiar.org The APSIM Model • APSIM version 7.6 is a processed based model that has been used to study crop response to different management options, environmental conditions and genetic yield potentials. • APSIM has specific modules embedded in the software which are linked and operate together. • It runs at a daily time step and simulates crop growth and development, yield, soil water and nitrogen dynamics either for single crop or crop rotations in response to climatic and management changes. • APSIM is capable of carrying out simulation studies for various farming systems and weed competition
  9. 9. www.iita.org I www.cgiar.org Model Calibration ➢ Calibration • Model calibration involves the modification of some model parameters such that data simulated by the error-free model fits the observe data. • Calibration Experiment • Trial sites (three Agricultural Research Farm) representing two different agro-ecologies – BUK Kano – ABCOA Danbatta – IAR Samaru Zaria
  10. 10. www.iita.org I www.cgiar.org Model Calibration ➢ Input data for model calibration • Daily climatic conditions – Rain- rainfall (mm), – solar radiation, MJ/m2, – maxT and (minT) • Soil profile information – Physical and Chemical properties • Crop growth parameters – Phenology, morphology, yield and yield component
  11. 11. www.iita.org I www.cgiar.org Result of model calibration Parameters Unit EVDT IWDC2 Estimated days from end of juvenile stage to flower initiation oC 20 20 Thermal time from seedling emergence to end of juvenile stage oC 190 240 Thermal time from flowering stage to maturity oC 540 870 Maximum head grain number 670 720 Thermal time from flag leaf development to flowering oC 8.0 8.0 Thermal time from flowering to start of grain filling oC 120 120 Thermal time from maturity to harvest oC 1 1 Genetic coefficients for two maize cultivars 2009EVDT and IWDC2 calibrated in APSIM
  12. 12. www.iita.org I www.cgiar.org Result of model calibration Location 2009 EVDT IWDC2 Flowering Maturity Flowering Maturity Simulated Observed Simulated Observed Simulated Observed Simulated Observed Kano 50 51 80 81 56 56 103 103 Danbatta 47 49 76 79 53 54 98 101 Zaria 53 54 85 82 62 59 112 105 Model calibration indices RMSE(days)a 1.41 2.52 0.71 4.24 RMSEn (%)b 2.75 3.12 1.83 4.12 R2(1:1) c 0.99 0.94 0.99 0.97 CRM 0.97 0.99 1.0 1.0
  13. 13. www.iita.org I www.cgiar.org Model Validation ➢ Model validation • This involves confirming that the calibrated model closely represent the real situation. • Experiment for model validation – 2 locations (Kubwa-Abuja ) in Southern Guinea Savanna and (Samaru-Zaria) in the Northern Guinea Savanna under rain-fed conditions for two years – 5 nitrogen rates were used (0, 30, 60, 90, 120 kgNha-1)
  14. 14. www.iita.org I www.cgiar.org Statistical indices for model validation
  15. 15. www.iita.org I www.cgiar.org Result of Model validation Abuja-Grain yield The model accurately predicted grain yield (Kgha-1) of both varieties with low RMSE-values (below 10% of mean) and high r-square (above 0.8).
  16. 16. www.iita.org I www.cgiar.org Results of Model validation Samaru-Zaria The model accurately predicted grain yield (Kgha-1) of both varieties with low RMSE- values (below 10% of mean) and high r-square (above 0.8).
  17. 17. www.iita.org I www.cgiar.org Model application to simulate maize yield response to N • 2 locations – Kano in the Sudan Savanna – Zaria in the Northern Guinea Savanna
  18. 18. www.iita.org I www.cgiar.org Model application to simulate maize yield response to N • Input data for the long term simulation – Daily maximum and minimum temperatures, rainfall and solar radiation corresponding to the period 1990- 2016 – Soil profile for each location – Simulations were run under rain-fed conditions, varying nitrogen application
  19. 19. www.iita.org I www.cgiar.org Cummulative probability of maize yield response to N Samaru- Zaria
  20. 20. www.iita.org I www.cgiar.org Cumulative probability of maize yield response to N BUK-Kano
  21. 21. www.iita.org I www.cgiar.org Conclusion • The model was reasonably calibrated and evaluated for the two varieties with all statistical indices within the acceptable range. • In Zaria 2009 EVDT did not significantly respond to N applied at rates higher than 90 kg N ha-1 while IWD C2 produced optimum yield at the rate of 120 kg N ha-1. • However, in Kano, both varieties did not respond significantly to N applied beyond 90 Kg N ha-1. • The results suggests the need to have a range of fertilizer recommendations to be applied based on seasonal weather forecast, soil type and the variety.
  22. 22. www.iita.org I www.cgiar.org Thank you for listening

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