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.