Presented by Abdul Rahman Nurudeen(IITA), Bekele Kotu(IITA), Gundula Fischer(IITA), Kipo Jimah(IITA), Francis Muthoni(IITA), Williams Attakora(CSIR-SARI), Addah Wesseh(UDS) at Africa RISING Ghana Country Planning Meeting, Tamale, Ghana, and Virtual, 24 - 25 June 2020.

1. Agronomy and crop-livestock interaction
activities in Ghana 2019/20
Abdul Rahman Nurudeen1, Bekele Kotu1, Gundula Fischer1, Kipo Jimah1, Francis
Muthoni1, Williams Attakora2, and Addah Wesseh3
1International Institute of Tropical Agriculture (IITA), 2Council for Scientific and
Industrial Research - Savanna Agricultural Research Institute (CSIR-SARI), and
3University for Development Studies (UDS)
Africa RISING Ghana Country Planning Meeting
24 - 25 June 2020
Tamale, Ghana & Virtual

2. Team contribution to outcome(s) and output(s)

3. GH111-1901: Cowpea living mulch effect on weed
control, soil properties and maize yield
Objectives: Determine the effect of cowpea living mulch on: (i) Soil properties, (ii) Weed
control, and (iii) Maize grain yield

4. Key research findings

5. Team deliverables
Deliverables Means of
verification
Delivery
date
Status
1. Data on agronomic and gender
preference for technology
Project semi-
annual report
Mar. 2020 Completed and
submitted
2. Cost-benefit and labor input data Project semi-
annual report
Mar. 2020 Completed
3. Technology extrapolation domain Maps Aug. 2020 On-going
4. Database on living mulch in maize Dataverse Dec. 2020 On-going
5. Paper published: Cowpea living-
mulch effects on maize grain yield,
soil moisture dynamics, weed
control and diversity
Workshop
proceeding
Land Use Policy
Dec. 2021 1. Abstract accepted for
presentation at PHAB
conference, Sept.
2020, The Hague,
Netherland.
2. Abstract submitted to
AAS annual meeting.
3. Journal publication:
work in progress

6. Team contribution to SIAF
Table 1. Maize and cowpea grain yields and weed biomass as affected by cowpea living mulch
system in northern Ghana
Mulching Grain yield (t/ha) Weed biomass (g/m2)
System NR UE WE Mean NR UE UW Mean
Control 1.9a 1.8a 2.4a 2.1a 205.2a 172.2a 67.4a 148.3a
MCSD 2.0a 1.2b 1.9b 1.7a 73.8b 67.9b 45.4b 62.4b
MC1W 2.2a 1.6a 2.2a 2.0a 77.6b 79.9b 50.6b 69.4b
MC2W 2.4a 1.5a 2.6a 2.2a 70.2b 93.4b 47.8b 70.5b
s.e.m 0.23 0.09 0.18 0.12 4.55 10.54 2.77 7.34
P-value 0.5495 0.0010 0.0540 0.0685 <.0001 <.0001 <.0001 <.0001
Productivity domain

7. Economic domain
Fig. 1. Labor requirement as affected by cowpea living mulch system at planting and
weeding in northern Ghana
0
20
40
60
80
100
120
140
160
180
Control MC2W MC1W MCSD
Labortime(hr/ha)
Planting
Weeding

8. Environment domain
Table 2. Cowpea living mulch system effect on soil temperature and
moisture in northern Ghana
Mulching Soil Temperature (⁰C) Soil Moisture (cm3/cm3 x 10-2)
System Vegetative Flowering Harvest Vegetative Flowering Harvest
Control 33.9a 31.8a 32.1a 4.7a 5.7c 5.5a
MCSD 31.5c 30.8b 31.0b 3.8a 8.3a 5.3a
MC1W 32.8b 31.3ab 31.1b 3.9a 5.9bc 5.5a
MC2W 32.6b 30.5b 30.7b 3.9a 7.3ab 4.8a
s.e.m 0.30 0.19 0.23 0.20 0.30 0.30
P-value 0.0006 0.0178 0.0067 0.0678 0.0035 0.612

9. Human domain
Table 3. Cowpea living mulch system effect on calorie and protein production in northern Ghana
Mulching
System
Calorie (kcal/ha x106) Protein (g/ha x105)
NR UE UE Mean NR UE UE Mean
Control 7.0a 6.6a 8.9a 7.5a 2.0b 1.7a 2.3d 2.0b
MCSD 5.4a 4.3c 6.8b 5.5b 3.4a 1.7a 6.5a 3.8a
MC1W 6.1a 5.7ab 7.8ab 6.5ab 3.1a 1.8a 5.4b 3.4a
MC2W 6.7a 5.5b 9.3a 7.2a 3.5a 1.6a 4.6c 3.3a
s.e.m 0.72 0.34 0.65 0.41 0.29 0.09 0.26 0.28
P-value 0.4370 0.0005 0.0397 0.0044 0.0044 0.7529 <.0001 <.0001

10. Social domain
Fig. 2. Farmer preference for cowpea living mulch system in northern Ghana
0
50
100
150
200
250
300
350
400
Female Male Female Male Female Male Female Male
Upper East Northern Upper West Total
Numberoffarmers
Control
MCSD
MC1W
MC2W

11. Fig. 3. Matrix scores for cowpea living mulch systems in northern Ghana
0
10
20
30
40
50
60
70
80
90
100
Male Female Male Female Male Female Male Female Male Female
Productivity
(Maize yield
Economic
(Profitability)
Environment
(Soil fertility)
Human (Cowpea
consumption)
Social (Reduced
labor time)
Numberoffarmers(%)
Number of HH=60Sole maize Living mulch

12. Scaling efforts
• A total of 128 farmers have been trained and hosted the evaluation of cowpea living mulch
trial demos (0.4 ha land/farmer) across the 3 northern regions.
• The 128 farmers field demos were conducted in collaboration with DDA’s.
• A total of 1,492 farmers have also been reached with cowpea living mulch technology
during community field days in the 3 northern regions.
• A total of 641 senior high school pupils have reached with cowpea living mulch during
community field days across the 3 northern regions.

13. Future direction of research
• Pull data from the different domains together for joint journal publication on sustainable
intensification of cowpea living mulch using the SIAF.
• Develop cowpea living mulch extrapolation domain map for Ghana and West Africa.
• Use crop model for scenario analysis in the context of farming system.

14. Fig. 4. System dynamics for CPLM technology
Evaluate system wide implications for wider adoption of CPLM in Northern Ghana

15. Fig. 5. Trends of rainfall and temperature in northern Ghana
 Significant increase in rains
detected in October in Northern
& Upper East regions.
 Non-significant drying trend in
Northern region from May –
August.
Muthoni, F. K. (2020). IEEE JSTARS, 13(1), 2960 -2973 doi:10.1109/JSTARS.2020.2997075

16. Objective: Determine the effect fertilizer type and time of application on: (i) Growth and yield of
maize, (ii)Fertilize use efficiency, and (iii) GHG emissions
GH111-1902: Optimizing on-farm nitrogen fertilizer
use efficiency under rain-fed condition

17. Key research findings
1. Application of basal NPK fertilizer at planting increased (P<0.01) maize grain yield, calorie
production and nitrogen use efficiency compared with the conventional method.
2. The new fertilizer blends exhibit lower NH3 fluxes compared to the compound fertilizers.
3. This technology is suitable for smallholder farmers in the 3 northern savanna regions and
similar agroecology within West Africa especially areas with high erratic rainfall distribution.
4. Trade-offs of labour dynamics and profitability of time of basal fertilizer application.

18. Team deliverables
Deliverables Means of
verification
Delivery
date
Status
1. Data on agronomic
and gender preference
for technology
Project semi-
annual report
Mar. 2020 Completed and submitted
2. Database on
Optimizing on-farm N
use efficiency
Dataverse Dec. 2020 On-going
3. Published paper:
Optimizing on-farm N
fertilizer use efficiency
under rain-fed
condition
Agronomy Journal Dec. 2021 Agronomic experimentation still in
progress: First year data completed,
waiting for additional year of
agronomic experiment to have stable
data for publication

19. Team contribution to SIAF
Time of fertilizer Grain yield (t/ha) NUE (kg/kg N)
application NR UE UW NR UE UW
Planting 2.3a 2.1a 4.3a 17.8a 17.3a 39.2a
Conventional 1.4b 1.7b 2.4b 9.3b 13.1b 19.9b
Planting + 2 WAP 2.1a 1.8b 3.8a 16.0a 14.7b 33.4a
s.e.m 0.11 0.08 0.27 1.01 0.73 2.69
P-value 0.0076 0.0165 0.0154 0.0069 0.0085 0.013
Table 4. Effect of time of application of basal fertilizer on maize grain yield and N use efficiency in
northern Ghana
Productivity domain

20. Environment domain
Fig. 6. Effect of time of basal fertilizer application on NH3 flux in northern Ghana
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1 3 5 7 9 11 13 15 17
ugNH3m-2S-1
Days of Measurement
a. Conventional
New Blend
Compound
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
1 2 3 4 5 6 7 8 9 1011121314151617
ugNH3m-2S-1
Days of Measurement
b. At planting
New Blend
Compound

21. Fig. 7. Maize calorie production as affected by
time of basal fertilizer application in northern
Ghana.
Fig. 8. Farmers capacity to experiment applying
basal fertilizer at planting in northern Ghana.
0
2
4
6
8
10
12
14
16
18
UE NR UW Ave.
Calorieproduction(kcal/hax106)
Conventional
Planting+2WAP
Planting
74% 89%
86%
83%
0
20
40
60
80
100
120
140
160
180
NR UE UW Total
Numberoffarmers
Male
Female
Human domain

22. Social domain
Fig. 9. Farmer preference for time of basal fertilizer application in maize cropping system
in northern Ghana
0
50
100
150
200
250
300
350
Female Male Female Male Female Male Female Male
Upper East Northern Upper West Total
Numberoffarmers
At planting
Conventional

23. Scaling efforts
• A total of 211 farmers have been trained and hosted the evaluation of N fertilizer trial
demos (0.4 ha land/farmer) across the 3 northern regions.
• The 211 farmers field demos were conducted in collaboration with DDA’s.
• A total of 848 farmers have also been reached with N fertilizer technology during
community field days in the 3 northern regions.
• A total of 283 senior high school pupils have reached with cowpea living mulch during
community field days across the 3 northern regions.

24. Future direction of research
• First year of agronomic trial completed but require another year data to get stable agronomic
data.
• Collect data on labour dynamics and profitability of time of basal fertilizer application during
second year of agronomic study.
• Use crop model to evaluate the resilience of time of basal fertilizer application to drought in
maize-based cropping system.

25. Maize leaf strip cropping system
Objectives: Determine the effect of leaf stripping as supplementary feed on: (i) In vitro digestibility of
organic matter, and (ii) Growth of sheep

26. Key research findings
1. Stripping of maize leaves at tasseling and silking did not affect (P>0.05) maize grain yield.
2. The feed quality of maize leaves striped at tasseling was not significantly different from that of
maize leaf stripped at silking.
3. Feeding maize leaf strippings as supplementary feed to sheep increases (P<0.01) daily growth
of sheep during cropping season.
4. Maize leaf stripping provides about 45% of livestock feed during the cropping season
especially for FHH and female parent.
5. This technology is suitable for smallholder farmers in the 3 northern savanna regions and
similar agroecology within West Africa especially areas with livestock feed scarcity during
cropping season.
6. Profitability and tradeoffs of maize leaf stripping.

27. Team deliverables
Deliverables Means of verification Delivery date Status
1. Data on maize leaf stripping
feeding trial
Project semi-annual
report
Mar. 2020 Completed
and submitted
2. Database on maize leaf
stripping technology
Dataverse Dec. 2020 On-going
3. Paper published: Leaf stripping
to maximize food and feed yields
from maize-based cropping
systems in northern Ghana
Experimental
Agriculture
Dec. 2021 Draft paper in
progress

28. Team contribution to SIAF
Productivity domain
Table 5. Effect of maize leaf stripping as supplementary feed on In vitro OMD and growth
performance of sheep in northern Ghana.
Item Control Leaf stripping s.e.m P-value
In Vitro OMD (48hrs, %DM) 44.8b 48.1a 3.54 0.0014
Initial weight (kg) 15.3a 15.0a 0.63 0.7770
Final weight (kg) 13.6b 16.8a 0.56 0.0010
Weight gain (kg) -1.7b 1.8a 0.56 0.0010
ADG (g/day) -22.9b 22.1a 7.78 0.0010

29. Economic domain
Fig. 10. Labor type and cost of labor for harvesting pasture for livestock feeding during cropping
season in northern Ghana

30. Human domain
0
10
20
30
40
50
60
70
80
90
Female Male Total
NumberofHH(%)
Number of HH = 60
Fig 11. Number of households continuing maize leaf stripping technology after project
support in northern Ghana

31. Social domain
Fig. 12. Livestock holding and use of maize leaf stripping for livestock feeding during cropping
season in northern Ghana

32. Scaling efforts
• A total of 145 farmers have been trained and hosted the evaluation of maize leaf stripping
trial demos (0.4 ha land/farmer) across the 3 northern regions.
• The 145 farmers field demos were conducted in collaboration with DDA’s.
• A total of 644 farmers have also been reached with maize leaf stripping technology during
community field days in the 3 northern regions.
• A total of 358 senior high school pupils have reached with maize leaf stripping technology
during community field days across the 3 northern regions.

33. Future direction of research
• Cost-benefit analysis of maize leaf stripping feeding experiment.
• Evaluate the effect of maize leaf stripping technology on boy-child education.
• Pull data from the different domains together for joint journal publication on sustainable
intensification maize leaf stripping using the SIAF.
• Use models to evaluate tradeoffs and synergies for maize leaf stripping technology.

34. Research progress towards outcome 1.1
• Tested 4 different agronomic trials with 52 treatments.
• Established 48 community-based technology parks.
• Established 876 upscaling demos on 0.4 ha of land/ farmer.
• Trained 3844 farmers and 1282 high school agricultural science students on improved crop
varieties and good agronomic technologies.
• Organized 36 community field days.
• Graduate students trained: MSC (2), and BSC (1).
• Publication: Journals (9), Project reports (9), conference presentations (6)

35. Grain yield (kg/acre) Grain yield (kg/acre)
Crop Baseline Upscaling Change (%) Baseline Technology Park Change (%)
Maize 314 713 127 314 814 159
Groundnut 229 476 108 229 642 180
Cowpea 155 193 25 155 212 38
Soybean 249 454 82 249 554 122
Table 6. Effect of Africa RISIING technologies on yield of crops in northern Ghana

36. Africa Research in Sustainable Intensification for the Next Generation
africa-rising.net
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