Ghana Ghana

Agriculture in Ghana

Agriculture accounts for about 28% of Ghana's Gross Domestic Product and employs more than half of the workforce, mainly small landholders. About 7.85 Mha is under cultivation. Out of this only 0.2% is under irrigation. Cocoa, oil palm, rubber and citrus constitute the major cash crops. The major starchy staples include maize, cassava, plantain, yam, cocoyam, rice, sorghum and millet and occupy a total land area of 3.40Mha, representing about 43% of total cultivated land. 

Average (2006-2011) production, harvested area and yield of major food crops in Ghana

Crop

Yield (t ha-1)

Harvested Area (ha)

Total Production (t)

Cassava

14.01

846,772

 

            11,863,768

Yam

16.35

361,273

 

                   5,192,053

Plantain

10.6

317,572

 

                   3,365,251

Cocoyam

6.55

233,946

 

                  1,531,673

Maize

1.68

899,767

 

                   1,508,964

Sorghum

1.12

261,262

 

                     293,804

Millet

1.13

181,153

 

                186,717

Rice

2.30

151,354

 

                347,380

Source: SRID, MoFA

There are six agro-ecological zones in Ghana: Sudan Savannah, Guinea Savannah, Coastal Savannah, Forest/Savannah transitional zone, Deciduous Forest zone and the Rain Forest zone. Total annual rainfall ranges from 780 mm in the dry eastern coastal belt to 2,200 mm in the wet southwest corner of the country. The rainfall pattern is uni-modal in the Coastal, Sudan and Guinea Savannah zones, but bi-modal in the three remaining zones.

Soils in the forest zone which developed over granites and phyllite, are mainly acrisols and lixisols and are deep, easily tilled and offer very little resistance to root growth. Soil total nitrogen is low, soils are acidic (pH 5.0-6.2) and their available P is low. Soils in the Guinea and Sudan savannah zones which are mainly Lixisols, Luvisols and Plinthosols, are shallow to moderately deep, are medium to light textured, and are developed over voltaian sandstones, granite, phyllite and schists. The soils are generally low in organic carbon and nitrogen. Nitrogen and P supply are less in the savannah zones than in the forest zones. A detailed description of soils in Ghana can be found in Brammer (1962) and Obeng (1975).

Agriculture in Ghana is predominantly on smallholder basis with about 90% of the farmers cultivating less than one hectare. It is characterized by traditional methods of farming using hoe and cutlass. There is little mechanization, except in the forest/savannah transitional and the Guinea Savannah zones, where mechanization involving the use of tractor for land preparation is practised. Bullock land preparation is also practised in the Sudan savannah zone.

Cereal crops (mainly maize, sorghum and millet) are produced in annual single-crop systems in the lower rainfall areas in the three northern regions. Maize is produced in annual single-crop systems in the higher rainfall area in the southern forest zone and in annual double-crop systems in the forest/savannah transitional zone. Typical double-crop systems in this zone include maize-maize, maize-cowpea and groundnut-maize. In the three northern regions, sorghum and millet are often intercropped with cowpea and/or maize and in the southern forest zone maize is often intercropped with one or more other crops such as cassava, cocoyam and plantain.

Data sources and their use

Data that are used for the yield gap analyses for Ghana, are given in the following.  More information about the applied GYGA approaches can be found at:

http://www.yieldgap.org/web/guest/methods-overview

Harvested area and actual yields

District-level data on annual actual yields were retrieved from the Ghana Ministry of Food and Agriculture (http://mofa.gov.gh/site/). Data from the period xxx till xxx have been used to calculate average actual yields per buffer zone. This has been done as follows: (a) determine per district the dominant climate zone; (b) calculate the average yield per buffer zone (via weighted averaging) based on the actual yields in districts that first, have a dominant climate which is similar to the climate of the buffer zone and second, are at least partly within the buffer zone.

Harvested areas were retrieved from the HarvestChoice SPAM crop distribution maps (You et al., 2006, 2009).

 Soil data

Soil data have been derived from the ISRIC-WISE database (Batjes, 2012). We have used the total available soil moisture fraction (i.e. difference in moisture contents between field capacity and wilting point) per soil unit and the maximal rooting depth from this database. If the country agronomist has specified a smaller rooting depth for the same soil unit, we have used this value. Next, available soil moisture fraction and maximal rooting depth have been multiplied to calculate the maximally available amount of soil moisture for the crop growth simulations per soil unit.

We selected the three dominant soil mapping units (i.e. SMUs, with per SMU the main soil units with their characteristics and relative area fractions within their SMU) for the growth simulations per crop type, based on their crop-specific cropped area (see Appendix B for the selected soil types per buffer zone and per crop) within each buffer zone (around the reference weather station, RWS).

Crop growth simulation have been done assuming the following soil and landscape characteristics: (a) no surface storage of water, (b) sufficient permeability of the soil to prevent soil saturation, (c) no ground water influence, (d) loss fraction of precipitation by surface runoff based on literature research as compiled in Appendix A (for this table we have assumed that management is optimal and mulching is applied); if no information about the slope anglewas available, we assumed a slope class of  2-6% to derive the surface runoff fraction, and (e) rooting depth is only limited by the soil in case that is indicated by ISRIC-WISE and/or the country agronomist.

Weather data and reference weather stations

Historical daily weather data sets have been collected from the Ghana Meteorology Agency. Weather sets are available for seven locations in Ghana and contain ten or more years of data. NASA-POWER (http://power.larc.nasa.gov/) was used as source of incident solar radiation. Years in which more than 20 consecutive days (10 consecutive days for precipitation) and/or more than 20% of the days are missing are left out. Linear interpolation has been used to fill missing data.

Based on crop harvested area distribution and the climate zones defined for Ghana (Van Wart et al., 2013), a total of 8 (rice and maize) and 3 (sorghum and millet) reference weather stations (RWS) were selected. RWS buffer zones accounted for 65, 75, 76 and 75% of total harvested areas  for rice, millet, sorghum, and maize, respectively of the national area for these crops. For rice and maize in two RWS buffer zones no actual weather data could be found, so the covered area decreased to 60% (rice) and 69% (maize).

Crop  and management information

Management practices for each RWS buffer zone were retrieved by the local country agronomist. Requested information included: dominant crop rotations and their proportions of the total harvested area, planting dates, dominant cultivar name and maturity, and actual and optimal plant population density. The crop and management information  per zone is given in Table 2.

Table 2.  Crop and management information for maize, sorghum and millet in different RWS buffer zones of Ghana as compiled by the country agronomist (Source: Dr. S. Adjei-Nsiah)

Zone

Crop rota-tion

Crop cycle

Water regime

% crop area under this rota-tion

Actual sowing date

Opti-mal sowing date

Cultivar & Growth duration1

Plant density
actual (per ha)

Plant density

Optimal (per ha)

Bolga-tanga

Single: Maize

Maize

Rainfed

100%

June

June 15

120 days

33,000

62,500

Bolga-tanga

Single: Millet

Late Millet

Rainfed

33.3%

End of May

June

150 days

55, 555

111,111

Bolga-tanga

Single: Millet

Early Millet

Rainfed

33.33%

End of May

-

90 days

55,555

111,111

Bolga-tanga

Single: Millet

Sorghum

Rainfed

33.33%

End of May

 

150 days

55,555

111,111

Bolga-tanga

Single: Sorghum

Sorghum

Rainfed

100%

May

June

140 days

44,400

88,800

Wa

Single: Maize

Maize

Rainfed

100%

June

-

120 days

33,000

62,500

Wa

Single: Millet

Millet

Rainfed

100%

Start of rains in May

June

150 days

55, 555

111,111

Wa

Single: Sorghum

Sorghum

Rainfed

100%

Start of rains in May

June

140 days

55,555

111,111

Kofori-dua

Double crop (maize-maize)

Maize

Rainfed

50%

April

April 15

120 days

37,037

62,500

Kofori-dua

Double crop (maize-maize)

Maize

Rainfed

50%

August

Aug. 15

120 days

37,037

62,500

Kofori-dua

Single Crop

Maize

Rainfed

50%

April

April 15

120 days

37,037

62,500

Kete Krachie

Double: (maize – maize)

Maize

Rainfed

37%

April

April 15

120 days

35,600

62,500

Kete Krachie

Double: (maize – maize)

Maize

Rainfed

37%

July

July 15

120 days

35,600

62,500

Kete Krachie

Single maize

Maize

Rainfed

63%

July

July 15

120 days

35,600

62500

Akim Oda/ Kusi

Double crop (maize-maize)

Maize

Rainfed

15%

April

April 15

120 days

40, 400

62,500

Akim Oda/ Kusi

Double crop (maize-maize)

Maize

Rainfed

15%

August-September

Aug. 15

120 days

40,400

62,500

Akim Oda/ Kusi

Single maize

Maize

Rainfed

85%

April

April 15

120 days

40,400

62,500

Sefwi-Bekwae

Single maize

Maize

Rainfed

80%

April

April 15

120 days

42,300

62,500

Sefwi-Bekwae

Single maize

Maize

Rainfed

20%

August

Aug. 15

120 days

42,300

62,500

Yendi

Single maize

Maize

Rainfed

100%

May 21-July 30

June 15-July 7

120 days

30,000

62,500

Yendi

Single sorghum

Sorghum

Rainfed

100%

July 1-August 1

July 1-July 15

120 days

20,000

88,888

1 Growth duration is assumed to be the period from sowing to maturity, of which the period from sowing to crop emergence is estimated at 5 days

Crop growth simulations and model calibration

Used crop growth models

The crop growth simulations for maize, sorghum and millet in Ghana have been carried out with the crop growth simulation model WOFOST version 7.1.3 (release March 2011) (Supit et al., 1994, 2012; Wolf et al., 2011).  For these WOFOST simulations we have used two sowing dates per RWS buffer zone, which are the actual and the optimal dates  given in Table 2 for each zone. Note that for the sowing dates, we use either the actual date (e.g. June 15)  if given, or otherwise the average date for the given period (e.g. given: June --> used date: June 15). For maize the crop growth model Hybrid-Maize has also been applied, and for rice ORYZA2000.

Data for model calibration

Based on experimental information reported in the literature,  we have compiled data for the main crop characteristics for maize, sorghum and millet growing in Ghana (Table 3). These characteristics can be considered representative for optimal (i.e. no water and no nutrient limitation) growing conditions in the different zones of Ghana. These crop characteristics have been used for testing and possibly calibrating the model parameters. The temperature sums required for phenological development per crop type in WOFOST are calibrated on the basis of the observed crop calendars (see Tables 2 and 4) and the climate conditions per RWS buffer zone in Ghana.

Table 3.  Crop characteristics for main crop types in Ghana to test and calibrate the WOFOST model parameters, being representative for a high-yield variety growing under optimal conditions with respect to water and nutrient supply and optimal management1

Crop, Zones in Ghana

Period from emergence to maturity (days)

Period frac-tions from emergence to flowering  and from flowering  to maturity (%)

LAI-max (m2 m-2)

Total biomass above-ground2

Yield2

Harvest index3

Grain maize, all zones

105 - 135

50% - 50%

4 to 7

10000 to 16000

5000 to 8000

0.45 to 0.55

Sorghum, all zones

135 - 145

55% - 45%

3 to 7

9000 to 14000

3600 to 5600

0.35 to 0.45

Millet, all zones

85 - 145

62% - 38%

3 to 7

9000 to 14000

2700 to 4200

0.25 to 0.35

1 Crop characteristics are based on Crop data for Ghana in Table 2, expert knowledge and experimental information, as reported by Kpongor, 2007; Morris et al., 1999; Obeng-Bio, 2010; Quansah, 2010; Adjei-Nsiah, 2012; DTMaize bulletin, 2013 and MacCarthy et al., 2009.

2 kg dry matter per ha

3 Yield / Total biomass above ground

We may assume that maize, sorghum and millet are in general produced in Ghana without application of irrigation water. However, to simplify the calibration of the model parameters related to crop growth and phenological development, we have done the crop model calibration for optimal conditions (see crop characteristics in Table 3). This means that water supply and nutrient supply are optimal to attain high yield levels and that crop protection and other management activities are all optimally performed.

Crop parameter sets for growth simulations

We used for the simulations with WOFOST the standard crop parameter sets as compiled by Van Heemst (1988). These parameter sets were later slightly adapted for western Africa. The new crop parameter sets are given in the files MAIZ-med-Ghana-GYGA.CAB, SORG-med-Ghana-GYGA.CAB and MILL-med-Ghana-GYGA.CAB for respectively, maize, sorghum and millet and are given in Appendix C. In the indicated files the following parameters are adapted for the GYGA-simulations: (a) temperature sums (TSUM1 and TSUM2) required for the modelled phenological development from crop emergence until flowering and from flowering to maturity, as calibrated  for the climate conditions and the crop data per RWS buffer zone in Table 2; the derived and applied TSUM1 and TSUM2 values for the different zones are given in Table 4; (b) maximal rooting depth, which is set at 150 cm for both maize, millet and sorghum; (c) life span of leaves (SPAN) for sorghum and maize has been increased to 42, whereas SPAN for millet has kept the same and similar value (=42); (d) correction factor for evapo-transpiration (CFET) has been increased from 1.0 to 1.1 for both maize, sorghum and millet (see Appendix C).

Table 4.  Temperature sums (TSUM1 and TSUM2) required for the modelled phenological development from crop emergence until flowering and from flowering to maturity as  calibrated for the climate conditions and the crop data  in Table 2 for maize, sorghum  and millet in the different RWS buffer zones of Ghana

Zone

Crop rota-tion

Crop cycle

Water regime

% crop area under this rota-tion

Used sow-ing date for model calibra-tion1

Growth duration2

TSUM1   (0C.d)

TSUM2

(0C.d)

Bolga-tanga

Single: Maize

Maize

Rainfed

100%

June 15

120 days

970

1000

Bolga-tanga

Single: Millet

Late Millet

Rainfed

33.3%

May 31

150 days

1070

950

Bolga-tanga

Single: Millet

Early Millet

Rainfed

33.33%

May 31

90 days

740

660

Bolga-tanga

Single: Millet

Sorghum

Rainfed

33.33%

May 31

150 days

1240

1230

Bolga-tanga

Single: Sorghum

Sorghum

Rainfed

100%

May 15

140 days

1130

1120

Wa

Single: Maize

Maize

Rainfed

100%

June 15

120 days

930

960

Wa

Single: Millet

Millet

Rainfed

100%

May 15

150 days

1070

950

Wa

Single: Sorghum

Sorghum

Rainfed

100%

 May 15

140 days

1130

1120

Kofori-dua

Double crop (maize-maize)

Maize

Rainfed

50%

April 15

120 days

930

960

Kofori-dua

Double crop (maize-maize)

Maize

Rainfed

50%

August 15

120 days

930

960

Kofori-dua

Single Crop

Maize

Rainfed

50%

April 15

120 days

930

960

Kete Krachie

Double: (maize – maize)

Maize

Rainfed

37%

April 14

120 days

950

1080

Kete Krachie

Double: (maize – maize)

Maize

Rainfed

37%

July 314

120 days

920

1050

Kete Krachie

Single maize

Maize

Rainfed

63%

July 15

120 days

920

1050

Kusi3

Double crop (maize-maize)

Maize

Rainfed

15%

April 15

120 days

900

950

Kusi3

Double crop (maize-maize)

Maize

Rainfed

15%

September 1

120 days

900

950

Kusi3

Single maize

Maize

Rainfed

85%

April 15

120 days

900

950

Sefwi-Bekwae

Single maize

Maize

Rainfed

80%

April 15

120 days

930

1060

Sefwi-Bekwae

Single maize

Maize

Rainfed

20%

August 15

120 days

900

1030

Yendi

Single maize

Maize

Rainfed

100%

June 25

120 days

870

1010

Yendi

Single sorghum

Sorghum

Rainfed

100%

July 16

120 days

1000

880

1 For the calibration the actual sowing date or the mean of the actual sowing dates from Table 2 is used

2 Growth duration is assumed to be the period from sowing to maturity, of which the period from sowing to crop emergence is estimated at about 5 days

3 Weather data used from Kusi to replace Akim Oda

4  Sowing dates for first and second maize crops are made resp. 15 days earlier and 15 days later to make the double maize rotation possible

Initialization of available soil moisture for simulation

For single cropping systems (i.e. one crop grown per year) and for the first crop in double cropping systems the simulation of the soil water balance has been started 90 days before the sowing date and thus generally in the dry season. At this start of the simulation the total amount of available soil moisture is set at 5 cm (i.e. soil moisture content being one third of the water holding capacity between field capacity and wilting point, thus: 0.33 *(SMFC-SMWP) ).

For double cropping systems the simulation of the soil water balance for the second crop has been started 30 days before the sowing date, and thus in the wet season and often after the maturity of the previous crop. At this start of the simulation the total amount of available soil moisture is set at 10 cm (i.e. soil moisture content set at roughly 0.7 * (SMFC –SMWP) ).

Calculation of mean water limited yield level and yield gap per buffer zone

Crop growth simulations for the different RWS-soil type-crop type-sowing date combinations (see some combinations in the rerun files with resp. CLFILE-SOFILE-CRFILE-IDSOW-ISYR (=year) given in Appendix D) in Ghana have been done for both potential (=irrigated) and water limited (=rain fed) conditions, to indicate the degree that yield levels may increase by application of irrigation water.

Crop production systems in Ghana are mainly rain fed. Hence,  only the water limited yields (Yw) have been used to calculate the yield gap. The mean Yw values per crop type per RWS buffer zone were calculated from the Yw values simulated for each crop type-sowing date-crop rotation-soil type combination per zone, weighted to their relative areas.

Next, the yield gap per RWS buffer zone is calculated as the difference between the mean Yw value per zone and the mean actual yield per zone. Note that the time period of the actual yields and that of the Yw values is partly different (i.e. mean of actual yields based on yields from 2005 up to and including 2011 and mean of simulated yields based on simulations for the available weather data between 1991 and 2010, covering roughly half of these years).

 

References

Adjei-Nsiah, S., 2012. Response of maize (Zea mays L.) to different rates of palm bunch ash application in the semi-deciduous forest agro-ecological zone of Ghana. Applied and Environ-mental Soil Science, Volume 2012, Article ID 870948, 5 pages, doi:10.1155/2012/870948.

Batjes, N.H., 2012. ISRIC-WISE derived soil properties on a 5 by 5 arc minutes grid. ISRIC report no. 2012-1, ISRIC, Wageningen, The Netherlands.

Brammer, H. (1962). Soils of Ghana. Pp. 88-126. In Brian Wills (Ed.). Agriculture and Land Use in Ghana. London, Oxford University Press.

DTMaize bulletin, 2013.  A Quarterly Bulletin of the Drought Tolerant Maize for Africa (DTMA) Project. Volume 2, no. 1, March 2013, CIMMYT, Narobi, Kenya

Kpongor, 2007 (PhD thesis). Spatially explicit modeling of sorghum (Sorghum bicolor (L.) Moench) production on complex terrain of a semi-arid region in Ghana using APSIM

MacCarthy, D.S., Sommer, R., Vlek, P.L.G., 2009. Modeling the impacts of contrasting nutrient and residue management practices on grain yield of sorghum (Sorghum bicolor (L.) Moench) in a semi-arid region of Ghana using APSIM. Field Crops Research 113, 105–115.

Ministry of Food and Agriculture (2012). Agriculture in Ghana, Facts and Figures. SRID, MoFA, Accra.

Morris, M.L., R. Tripp, and A.A. Dankyi. 1999. Adoption and Impacts of Improved Maize Production Technology: A Case Study of the Ghana Grains Development Project. Economics Program Paper 99-01, CIMMYT, Mexico, D.F.

Obeng, H.B. 1975. Soils of the Savanna Zones of Ghana – Their physico -chemical characteristic classification and management, pp. 11-23. In: H.B. Obeng and P.K. Kwakye (Eds.). Savanna soils of the sub-humid and semi-humid and semi-arid regions of Africa and their management ISSS Comm. I, IV, V and VI – State Publishing Corporation Accra. Ghana.

Obeng-Bio, E., 2010. Selection and ranking of local and exotic maize (Zea mays L.) genotypes to drought stress in Ghana. Msc.-thesis, Kumasi, Ghana.

Quansah, G.W., 2010; Effect of organic and inorganic fertilizers and their combinations on the growth and yiled of maize in the semi-deciduous forest zone of Ghana. MSc.-thesis, Kumasi, Ghana.

Supit, I., Hooijer, A.A., Van Diepen, C.A. (Eds.), 1994. System description of the WOFOST 6.0 crop simulation model implemented in CGMS. European Communities (EUR15956EN), Luxembourg.

Supit, I., Van Diepen, C.A., De Wit, A.J.W., Wolf, J., Kabat, P., Baruth, B., Ludwig F., 2012. Assessing climate change effects on European crop yields using the Crop Growth Monitoring System and a weather generator. Agric. Forest Meteorol. 164, 96-111.

Van Heemst, H., 1988. Plant data values required for simple and universal simulation models: review and bibliography. Simulation reports CABO-TT, Wageningen, The Netherlands..

Wolf, J., Hessel, R., Boogaard, H.L., De Wit, A., Akkermans, W., Van Diepen, C.A., 2011. Modeling winter wheat production over Europe with WOFOST – the effect of two new zonations and two newly calibrated model parameter sets. In: Ahuja, L.R., Ma, L. (Eds.), Methods of Introducing System Models into Agricultural Research. Advances in Agricultural Systems Modeling 2: Trans-disciplinary Research, Synthesis, and Applications, ASA-SSSA-CSSA book series, 297-326.

Appendices

Appendix A  Fraction of precipitation lost by surface runoff (based on literature review)

Surface runoff fraction of total seasonal precipitation (in %) for soils that are cultivated with cereals and are mulched

Drainage class,  

Slope angle, in %

Very poor

Insufficient

Moderate

Well drained

Extremely well drained

0-2

10

6.7

3.3

0

0

2-6

13.3

10

6.7

3.3

0

6-10

16.7

13.3

10

6.7

3.3

>10

20

16.7

13.3

10

6.7

 

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Country agronomist Country agronomist


Samuel Adjei-Nsiah

University of Ghana