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Simulating Cropping Systems in the Guinea Savanna Zone of Northern Ghana with DSSAT-CENTURY J. B. Naab 1, Jawoo Koo 2, J.W. Jones 2, and K. J. Boote 2,

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Presentation on theme: "Simulating Cropping Systems in the Guinea Savanna Zone of Northern Ghana with DSSAT-CENTURY J. B. Naab 1, Jawoo Koo 2, J.W. Jones 2, and K. J. Boote 2,"— Presentation transcript:

1 Simulating Cropping Systems in the Guinea Savanna Zone of Northern Ghana with DSSAT-CENTURY J. B. Naab 1, Jawoo Koo 2, J.W. Jones 2, and K. J. Boote 2, 1 Savanna Agricultural Research Institute (SARI) Tamale, Ghana, 2 Univ. of Florida,

2 Introduction To be useful to community decision makers, models must be capable of quantifying crop performance in the communities where they are to be used. Necessary to adapt the model to soils, climate and cropping systems of interest and to evaluate predictions from the model relative to local data

3 Objective Present progress in the adaptation of DSSAT- CENTURY to the cropping systems in two locations in Northern Ghana and its ability to simulate growth, yield and soil carbon sequestration.

4 Peanut experiments in 1997 & 1998 Cultivars: Chinese (90 d) & F-mix (120 d) Sowing dates: 3 or 4 sowing dates Complete growth analyses Detailed soil water measurements Used PNUTGRO model to simulate soil water balance & potential growth

5 J. Naab – Ghana, Two peanut cult. Simulated with no disease effect Simulated with input defoliation and leafspot injury Crop had no fungicide applied

6 J. Naab – Ghana, Two peanut cult. Simulated with no disease effect Simulated with input defoliation and leafspot injury Crop had no fungicide applied

7 Peanut Experiments: Simulation analyses suggested yield losses of 50 to 70% from disease effects. Leaf spot disease is common on peanut in Ghana, where fungicides are not used. Can peanut produce simulated yield levels with fungicide? Can the model predict this?

8 Materials and methods Same cultivars as in 1997 & sowing dates With (+) and without (-) fungicide applied Fungicide: Folicur and Abound Detailed growth analysis Used CROPGRO-peanut model to simulate growth under disease epidemics

9 Measurements Phenology data (flowering, 1 st pod and 1 st seed ) Time-series growth analysis of leaf, stem, pod and seed mass Defoliation and disease scores Yield and yield components (pod and seed yields, HI, threshing %, 100-seed weight)

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12 Maize Rotation Experiments ( ) Location: Nyankpala & Wa 3 cropping systems: continuous maize, maize-peanut, & peanut-maize Nitrogen levels: 0, 30, 60, 4 t/ha manure Yearly experiment files and annual measurement files setup Four sequence files for rotating crops were also setup for maize plots

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14 Yield variability in response to N fertilization

15 Yield variability in response to total cumulative rainfall during the crop season

16 Legume yield variability with total cumulative rainfall

17 Relationship between simulated nitrogen stress factor and rainfall amount

18 Simulated nitrogen leaching as a function of rainfall

19 Simulation of 2003 On-farm Maize Rotations Location: Nakor village, Wa Three farmers collaborated Four cropping systems studied: continuous -maize + low N (30kg/ha) -continuous maize + high N (80 kg/ha) -maize-peanut + 40 kg/ha N -peanut-maize+ 40 kg/ha N to maize

20 Measurements Initial soil carbon content Soil texture Biomass at final harvest Grain yield at final harvest

21 Calibration Changed genetic coefficients of maize based on observed biomass and grain yield. Photosynthetic factor for each field set=0.85 SLNF for each field was set = 1.0

22 Calibration continues.. Initial SOM pool fractions calibrated as follows: (i) On-farm experiments were simulated for 50 years using the reported SOM fractions for Mali cropping system condition (SOM1:SOM2:SOM3 = 0.02:0.41:0.57, Walen et al., 2002); (ii)Yearly carbon initially decreased, then stabilized after about 50 years; (iii) The SOM pool fractions were then obtained when soil carbon stabilized (SOM1:SOM2:SOM3 = 0.01:0.14:0.85).

23 Simulated and observed maize biomass in Nakor village, Wa, Ghana

24 Correlation between simulated maize biomass and top 20cm soil carbon content in three farmers' field

25 Total carbon sequestration in farmers fields

26 Conclusions Model able to predict peanut growth and yield under varying sowing dates, varieties, and leafspot disease epidermics after calibration It was reasonably accurate in simulating maize yield variability under different nitrogen regimes Simulated yield highly correlated with soil C; fertilizer use efficiency was lower for low soil C situations Simulation, after calibration, suggest that there is potential for soil C sequestration in maize cropping systems with fertilizer and manure.

27 Simulated maize biomass in Boakye's farm

28 Simulated maize biomass in Dramani's farm

29 Simulated maize biomass in Wounbuno's farm


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