Wheat production in three Sub-Saharan African countries Wheat production in three Sub-Saharan African countries

Countries: Ethiopia, Kenya and Tanzania

Introduction

Wheat is the second-most-produced cereal grain behind maize in the world (FAOSTAT, 2022). The global production volume of wheat came to about over 761 Mton in the marketing year of 2019/20 (FAOSTAT, 2022). Cereals like maize, rice and sorghum, millet and wheat are major staple foods for most people in Africa. Africa produces around 25 Mton of wheat on 10 Mha (FAOSTAT, 2022). The wheat area s makes around 8% of the area used for cereal production in Africa (FAOSTAT, 2022). Around 19 percent of all wheat lands in Africa are located in the three sub-Saharan countries (Table 1).

Table 1. Annual wheat harvest area in sub-Saharan Africa

Country

Annual wheat

harvest area (1000 ha)*

Percentage of total

 wheat lands in Africa

Ethiopia

1737.3

17.5

Kenya

134.9

1.4

Tanzania

58.5

0.6

Total

1930.7

19.4

* The data are from FAOSTAT for the period from 2015 to 2020

 

 

The crop model

The Python Crop Simulation Environment of WOFOST (WOrld FOod STudies) was used for the implementation (https://pcse.readthedocs.io/en/stable/). This model takes into account phenological development, leaf development, light interception, CO2 assimilation, root growth, transpiration, respiration and partitioning of assimilates (de Wit et al., 2019). Daily weather data, crop parameters, soil parameters, and management data are needed to run the model and simulate water-limited potential yield (Yw). The source of different data to do the simulations for each country were presented in Table 2.

Table 2.Sources of data to simulate rainfed wheat potential yield and for calculating the yield gap. Note: GYGA CA = GYGA country agronomist

  •  

Sowing window

Daily weather data

Cultivar thermal time requirment**

Soil data

Actual yield

  •  

GYGA CA

Measured and Propagated data*

  •  
  •  

GYGA CA

  •  

GYGA CA

Propagated data

  •  
  1.  
  1.  
  •  

GYGA CA

Propagated data

  •  
  1.  

GYGA CA

* Details are in Van Wart et al. (2015)

** Calculated based on the sowing, flowering and maturity timing information from the GYGA country agronomist using weather data and cardinal temperatures

*** AFrica Soil Information Service (Leenaars J.G.B., 2018)

Reference weather stations

The simulation was performed for 25 weather stations in the three? countries (Table 3). These were identified as reference weather stations for rainfed wheat in SSA countries based on the GYGA protocol (https://www.yieldgap.org/web/guest/methods-overview). SPAM (Spatial Production Allocation Model; https://www.mapspam.info/) maps, together with expert knowledge from agronomists and experts from these countries, were used to identify the rainfed wheat harvested area  (You et al., 2009, 2014). Following van Bussel et al (2015), a total of 25?? buffer zones were selected for rainfed wheat in SSA countries (Table 3, Fig. 1)

Figure 1. The location of the reference weather stations for rainfed wheat in Sub-Saharan African countries

Weather data

Weather data used in simulation included daily solar radiation, maximum and minimum temperatures, precipitation, vapor pressure deficit, and wind speed. Weather data for selected weather stations were subjected to quality control measures to fill in missing data and identify and correct erroneous values. In the case of those stations with only few years of weather data (Table 3), long-term weather data were generated based on correlations between measured weather data and NASA-POWER maximum and minimum temperatures (Van Wart et al., 2015). In the case of solar radiation and rainfall, the data from NASA-POWER were used without any correction (NASA, 2022). In the case of buffers without measure weather data at all, named the virtual stations (Table 3), uncorrected NASA-POWER data were used for all meteorological variables needed for crop modeling.

Soil information

Soil data was obtained from the AFSIS[1] (Leenaars et al., 2018). Soil moisture content at field capacity, soil moisture content at wilting point, not infiltrating fraction of rainfall, initial soil water, and maximum rootable depth of the soil were obtained as soil parameters for each weather station.

Management data

The simulations were done under water-limited conditions with no limitation for nutrients. So, the sowing date within the buffers of the weather stations was the main management input data needed to run the model. For this purpose, the common sowing windows of rainfed wheat for each buffer were retrieved through agronomists from the countries (Fig. 2). These sowing windows and an algorithm were used to estimate the sowing date for each year for each weather station. The algorithm calculates the amount of cumulative rainfall for seven consecutive days into the sowing window. The last day of this period resulting in more than 20 millimeter cumulative rainfall was considered as the sowing date. If there was no consecutive seven days with at least 20 millimeter rainfall into the sowing window, the last day of the sowing window was as the sowing date.

Crop parameters

The crop parameters consist of parameter names and the corresponding parameter values that are needed to parameterize the components of the crop simulation model. These are crop-specific values regarding phenology, assimilation, respiration, biomass partitioning, etc. The same crop parameters were used for the simulations in all the weather stations except the parameters for phenology. The phenological parameters including the thermal time requirement from emergence to flowering and from flowering to harvesting were calculated based on the observed data for sowing, emergence, flowering and harvesting using the cardinal temperature and weather data at each weather station (Table 3).

Figure 2. The sowing window to plant rainfed wheat at the weather stations in Sub-Saharan Africa countries; The name and information of each station are presented in Table 3.

 

Table 3. Rainfed wheat cropping system and management information and the thermal time requirement of the cultivars at each weather station

Country

Station name

Station ID*

Longitude

Latitude

Elev

Water regime

Cropping system

Cropping cycle

Sowing window

Thermal time requirement**

Start

End

From emergence

to flowering

From flowering

to maturity

Adet

Ethiopia

5000000

37.48

11.27

2240

Rainfed

Single: wheat

1

15-Jun

30-Jul

1310

1160

Ambo

Ethiopia

5000001

37.835

8.96

2100

Rainfed

Single: wheat

1

15-Jun

15-Jul

1210

1080

Ayira

Ethiopia

5000007

35.33

9.06

1700

Rainfed

Single: wheat

1

15-Jun

15-Jul

1300

1150

Butajira

Ethiopia

5000010

38.22

8.08

3252

Rainfed

Single: wheat

1

15-Jun

15-Jul

1130

1000

Debrezeit

Ethiopia

5000014

39

8.72

1900

Rainfed

Single: wheat

1

15-Jun

14-Jul

1150

1020

Kulumsa

Ethiopia

5000025

39.15

8

2241

Rainfed

Single: wheat

1

15-Jun

15-Jul

1050

940

Mekele

Ethiopia

5000026

39.47

13.52

1970

Rainfed

Single: wheat

1

15-Jun

15-Jul

1080

950

Melkassa

Ethiopia

5000027

39.33

8.4

1550

Rainfed

Single: wheat

1

15-Jun

15-Jul

1210

1080

Sheno

Ethiopia

5000032

39.3

9.344

2848

Rainfed

Single: wheat

1

1-Jul

31-Jul

890

790

Woliso

Ethiopia

5000035

37.97

8.55

2060

Rainfed

Single: wheat

1

15-Jun

15-Jul

1300

1150

Eth_rfwt2

Ethiopia

5000135

38.83

7.17

2675

Rainfed

Single: wheat

1

15-Jun

15-Jul

1200

1060

Eth_rfwt3

Ethiopia

5000136

40.11

7.77

2475

Rainfed

Single: wheat

1

15-Jul

15-Aug

1540

1360

Kakamega

Kenya

9000003

34.46

0.17

1399

Rainfed

Single: wheat

1

15-Aug

15-Sep

1150

1050

Kericho

Kenya

9000005

35.16

-0.22

1356

Rainfed

Single: wheat

1

15-Aug

15-Sep

1150

1050

Kitale

Kenya

9000008

34.96

0.97

1850

Rainfed

Single: wheat

1

15-Aug

15-Sep

1150

1050

Meru

Kenya

9000011

37.39

0.5

1044

Rainfed

Single: wheat

1

15-Sep

15-Oct

1150

1050

Nakuru

Kenya

9000012

36.6

-0.16

2557

Rainfed

Single: wheat

1

15-Aug

15-Sep

1150

1050

Eldoret

Kenya

9000015

35.3

0.48

2120

Rainfed

Single: wheat

1

1-Aug

31-Aug

1150

1050

Arusha

Tanzania

11000000

36.63

-3.32

1488

Rainfed

Single: wheat

1

5-Jan

3-Feb

1300

1200

Kia

Tanzania

11000003

37.07

-3.43

896

Rainfed

Single: wheat

1

4-Feb

5-Mar

1300

1200

Tan_rfwt1

Tanzania

11000125

36.92

-4.37

1313

Rainfed

Single: wheat

1

6-Dec

30-Dec

1300

1200

Tan_rfwt3

Tanzania

11000127

36.06

-2.88

914

Rainfed

Single: wheat

1

26-Jan

24-Feb

1300

1200

Tan_rfwt4

Tanzania

11000128

35.32

-3.65

1395

Rainfed

Single: wheat

1

5-Jan

3-Feb

1300

1200

Tan_rfwt5

Tanzania

11000129

37.52

-5.03

1041

Rainfed

Single: wheat

1

5-Jan

3-Feb

1300

1200

Tan_rfwt6

Tanzania

11000130

34.65

-3.54

1405

Rainfed

Single: wheat

1

6-Dec

30-Dec

1300

1200

* If the last three number of a station ID is more or equal to 100, the station is as a virtual station

** The cardinal temperatures to calculate the thermal time for sorghum are 0C as the base temperature, 30C or more than 30C  as the optimum temperature.

 

Reference

de Wit, A., Boogaard, H., Fumagalli, D., Janssen, S., Knapen, R., van Kraalingen, D., . . . van Diepen, K. (2019). 25 years of the WOFOST cropping systems model. Agricultural Systems, 168, 154-167. doi:10.1016/j.agsy.2018.06.018

https://pcse.readthedocs.io/en/stable. Accessed on 2022-06-10

https://www.fao.org/faostat/en/#data. Accessed on 2022-06-10

https://www.mapspam.info. Accessed on 2022-06-10

https://www.yieldgap.org/web/guest/methods-overview. Accessed on 2022-06-10

Leenaars, J.G., Claessens, L., Heuvelink, G.B., Hengl, T., González, M.R., van Bussel, L.G., Guilpart, N., Yang, H. and Cassman, K.G., 2018. Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa. Geoderma324, pp.18-36.

NASA. NASA-Agroclimatology methodology. Available at: https://power.larc.nasa.gov/data-access-viewer/. 2022. Accessed on April 10, 2022.

van Bussel LG, Grassini P, Van Wart J, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, Saito K, Cassman KG, van Ittersum MK. From field to atlas: upscaling of location-specific yield gap estimates. Field Crops Research, 2015, 177: 98-108.

Van Wart, J., Grassini, P., Yang, H., Claessens, L., Jarvis, A. and Cassman, K.G., 2015. Creating long-term weather data from thin air for crop simulation modeling. Agricultural and Forest Meteorology209, pp.49-58.

You L, Wood S, Wood-Sichra U, Wu W. Generating global crop distribution maps: From census to grid. Agricultural Systems, 2014, 127: 53-60.

You L, Wood S, Wood-Sichra U. 2009. Generating plausible crop distribution maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agricultural Systems, 99: 126-140.

 

[1] AFrica Soil Information Service

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