This study involves an assessment of the potential effects of climate change, as well as the direct fertilization effect of CO2 on crop yields in Cameroon. The methodology involves coupling the transient diagnostics of 2 atmosphere–ocean general circulation models, namely NASA/Goddard Institute GISS and the Hadley Centre’s HadCM3, to the CropSyst crop model to simulate current and future (2020, 2080) crop yields (bambara nut, groundnut, maize, sorghum and soybean) in 8 agricultural regions of Cameroon. For the future we estimate substantial yield increases for bambara groundnut, soybean and groundnut, and little or no change and even decreases of maize and sorghum yields, varying according to the climate scenario and the agricultural region. Maize and sorghum (both C4 crops) yields are expected to decrease by 14.6 and 39.9%, respectively, across the whole country under GISS 2080 scenarios. The results also show that the effect of temperature patterns on climate change is much more important than that of precipitation. Findings call for monitoring of climate change/variability and dissemination of information to farmers, to encourage adaptation to climate change.
This study uses the CropSyst model to investigate the potential impacts of climate change on five key crops in Cameroon.
The study uses the coupled climate scenario–crop model approach in which present and future climate conditions, generated by the selected climate models following 2 GHG emission scenarios, are integrated as inputs into the crop model so as to simulate crop growth, development and production. The ‘naive farmer’ scenario is used, meaning that all variables other than weather and atmospheric CO2 concentration, namely soil, cultivar and management characteristics, are held constant between present and future crop yield simulations. Though in reality farmers are able to make management changes to cope with an altered climate, representing the diversity of these adaptations within a modelling project was seen as adding an additional level of complexity to a study primarily aimed at determining the impacts of the climate on crop production.
Present and future crop yields are compared to evaluate the impacts of GHG-induced climate change on agriculture. The A-OGCM climate models used in this study are the coupled NASA/Goddard Institute GISS and HadCM3 of the British Hadley Centre. Both models are forced by the SRES A2 and B2 emission scenarios. The selection of AOGCMs was based on the general quality and reliability of the simulated current climate compared to observed data and data availability to generate baseline (1961 to 1990) and future climate scenarios (2020s, 2080s)
The general objective of this study is to examine the effects of long-term climate change on Cameroon crop agriculture and identify the adaptation options of agroecological systems using a simulation analysis. The specific objectives are to simulate and highlight the expected effects of various long-term climate change scenarios on future agricultural productivity and discuss policy design, research and extension in planning potentially effective adaptation options to mitigate negative climate change impacts.
This study used 8 sites in Cameroon that represent the variations within the country.
|
Location |
Latitude |
Longitude |
Elevation (m) |
Annual rainfall (mm) |
|
Bamenda |
6.05o |
10.1o |
1239 |
2378 |
|
Batouri |
4.47o |
14.37o |
656 |
1499 |
|
Garoua |
9.33o |
13.38o |
244 |
1090 |
|
Kribi |
2.95o |
9.89o |
16 |
2634 |
|
Maroua |
10.44o |
14.25o |
422 |
834 |
|
Ngaoundere |
7.34o |
13.57o |
1104 |
1514 |
|
Tiko |
4.08o |
9.37o |
52 |
3198 |
|
Yaounde |
3.83o |
11.51o |
760 |
1655 |
The country is characterized by highly contrasting physical features including 402 km of coastline and mountain ranges punctuated by peaks over 3,000m. Climate characteristics, reflecting the topography and latitudinal range, roughly follow a North-South gradient with the humid equatorial region in the south and semi-arid regions to the north. The equatorial zone stretches from 2 to 6o N covering the southern and the mountainous western part of the country. Its climate corresponds to the classical Guinean region, with the following subtypes: (1) the seaboard, e.g. Kribi and Tiko with abundant rainfall (2634 and 3198 mm yr-1 respectively); (2) the inland areas, e.g. Yaounde with total rainfall < 1660 mm yr-1. This climate subtype prevails over the southern part of the southern Cameroon plateau, extending into the east of the country around Batouri; (3) North of 6o N, the Sudanese-Sahelian subtype differs from the ‘inland’ with total rainfall decreasing from 1513 to 834 mm yr-1. Annual average temperature across the country varies between 20 and 29oC, and in the extreme North daily temperatures are usually between 25 and 34 oC.
The humid equatorial zone in the south favours the cultivation of cash crops such as oil palm, bananas, cocoa, rubber, plantains, and coffee and key food crops: maize (Zea mays L.), groundnut (Arachis hypogaea L.), sorghum (Sorghum bicolour (L.), bambara groundnut (Vigna subterranea L. Verdc) and soybean (Glycine max (L.) Merr). The semi-arid region to the north mostly favours the growth of millet, sorghum, maize and groundnuts. Growing season is related to the rainy season and planting is fine-tuned to very specific times of the year. In the equatorial zone comprising Bamenda, Batouri, Kribi, Tiko and Yaounde, there are two rainy seasons: the first is the ‘long’, being March-July with planting in March; and the second is the ‘short’, from August to November. In the tropical zone: Garoua, Maroua and Ngaoundere there is only one rainy season, from May to October and planting starts in May (Ndemah 1999). Table 1 shows geo-location of the studied sites and Appendix A describes the regional climatic differences within Cameroon, giving the reader enough information to identify the climatic spatial variation.
References:
Ndemah RN (1999) Towards an integrated crop management strategy for the African stalk borer Busseola fusca (Fuller) (Lepidoptera: Noctuidae) in maize systems in Cameroon. PhD Thesis, University of Hannover, Hannover, Germany. 145 pp
This study used the CropSyst cropping systems model developed at Washington State University.
This study did not require any context specific alterations to the CropSyst model.
This study was primarily undertaken by Richard Munang Tingem as part of his PhD with the Agriculture & Environmental Science Division, School of Bioscience, Nottingham University.
Results
Relative changes in the average yields of maize, sorghum, bambara groundnut, groundnut and soybean predicted between present (baseline) and future (2020s/2080s) climates are presented below:
|
|
GISS |
HadCM3 |
|||||||
|
|
Baseline |
A2 2020 |
A2 2080 |
B2 2020 |
B2 2080 |
A2 2020 |
A2 2080 |
B2 2020 |
B2 2080 |
|
Maize |
Yield (kgha-1) |
|
|
|
|
|
|
|
|
|
Bamenda |
1294 |
-24.7 |
-69.6 |
-22.9 |
-51.2 |
-6.7 |
-56.2 |
-5.9 |
-20.6 |
|
Batouri |
1488 |
0.9 |
-33 |
0.2 |
-17.8 |
13.6 |
-22.5 |
14.2 |
-8.2 |
|
Garoua |
1945 |
3.1 |
-16.1 |
4.1 |
-11 |
9.1 |
-12.1 |
11.2 |
-6.4 |
|
Kribi |
1835 |
18.9 |
9.6 |
19.4 |
13.1 |
25.4 |
12.3 |
25.9 |
15.3 |
|
Maroua |
2171 |
5.3 |
-10.5 |
6.9 |
-6.6 |
13.3 |
-8.1 |
10.6 |
-2.91 |
|
Ngaoundere |
2318 |
24.6 |
6.2 |
25 |
17.3 |
27.1 |
13.8 |
26.9 |
22 |
|
Tiko |
2447 |
12.6 |
-0.6 |
12.5 |
3.5 |
18.3 |
3.4 |
18.4 |
7.6 |
|
Yaounde |
2158 |
18.4 |
-2.7 |
20 |
7.8 |
24.1 |
3.5 |
24.1 |
12.3 |
|
Mean |
1957 |
7.4 |
-14.6 |
8.2 |
-5.6 |
15.5 |
-8.2 |
15.7 |
2.4 |
|
|
|
|
|
|
|
|
|
|
|
|
Groundnut |
|
|
|
|
|
|
|
|
|
|
Bamenda |
1017 |
-13.5 |
-41.6 |
-11.9 |
-30.1 |
1.9 |
-33.4 |
1.9 |
-22.7 |
|
Batouri |
996 |
38.4 |
21.9 |
30.4 |
30.0 |
51.3 |
47.1 |
57.8 |
50.6 |
|
Garoua |
995 |
15.7 |
-7.4 |
16.9 |
0.6 |
19.8 |
-1.2 |
23.2 |
6.6 |
|
Kribi |
557 |
109.0 |
113.0 |
109.0 |
108.7 |
110.0 |
108.7 |
109.2 |
108.9 |
|
Maroua |
1172 |
45.3 |
34.5 |
46 |
38.2 |
48.9 |
36.6 |
48 |
40.7 |
|
Ngaoundere |
1197 |
50.3 |
37.2 |
51 |
41.7 |
57.2 |
40.1 |
57.1 |
44.5 |
|
Tiko |
948 |
19 |
-1.8 |
25.6 |
12.1 |
35.2 |
16.8 |
32.3 |
21.5 |
|
Yaounde |
1106 |
8.1 |
-12.4 |
11.1 |
-2.8 |
18.6 |
-6.3 |
18.6 |
1.8 |
|
Mean |
998 |
34.0 |
17.9 |
34.8 |
24.8 |
42.9 |
26.1 |
43.5 |
31.5 |
|
|
|
|
|
|
|
|
|
|
|
|
Bambara |
|
|
|
|
|
|
|
|
|
|
Bamenda |
1160 |
31.2 |
1.2 |
32.9 |
17.3 |
42.5 |
13.2 |
43.3 |
23.5 |
|
Garoua |
1402 |
24.3 |
4.9 |
25.2 |
11.9 |
31 |
10.2 |
30.1 |
16.8 |
|
Maroua |
1310 |
37.2 |
25.9 |
37.8 |
29.5 |
41.3 |
28.2 |
40.4 |
32.1 |
|
Ngaoundere |
1571 |
52.5 |
46.8 |
53.4 |
49.1 |
58.3 |
48.7 |
57.1 |
50.5 |
|
Tiko |
1184 |
9.3 |
-5.1 |
2 |
6.4 |
20.5 |
12.5 |
28.2 |
11.2 |
|
Yaounde |
1193 |
21.5 |
3.9 |
24.6 |
12.8 |
31.6 |
9.6 |
31.6 |
16.8 |
|
Mean |
1303 |
29.3 |
12.9 |
29.3 |
21.2 |
37.5 |
20.4 |
38.5 |
25.2 |
|
|
|
|
|
|
|
|
|
|
|
|
Sorghum |
|
|
|
|
|
|
|
|
|
|
Garoua |
1311 |
-8.2 |
-35.7 |
-6.1 |
-28.5 |
1.3 |
-32 |
4.4 |
-21.9 |
|
Maroua |
1484 |
3.2 |
-20.1 |
6.3 |
-14.2 |
17.1 |
-16.2 |
14.6 |
-9.3 |
|
Ngaoundere |
1280 |
-16.6 |
-63.8 |
-12.3 |
-47.8 |
3.8 |
-53.5 |
3.4 |
-40.7 |
|
Mean |
1358 |
-7.2 |
-39.9 |
-4.0 |
-30.2 |
7.4 |
-33.9 |
7.5 |
-24.0 |
|
|
|
|
|
|
|
|
|
|
|
|
Soybean |
|
|
|
|
|
|
|
|
|
|
Bamenda |
572 |
57.6 |
27.9 |
58.5 |
38.8 |
68.7 |
34.2 |
78.9 |
45.5 |
|
Ngaoundere |
1169 |
27.9 |
5.5 |
29.6 |
12.6 |
39.5 |
10.9 |
39.5 |
18.8 |
|
Tiko |
110 |
126.9 |
130.4 |
127.7 |
134 |
153.6 |
148.2 |
145.5 |
162.4 |
|
Mean |
617 |
70.8 |
54.6 |
71.9 |
61.8 |
87.3 |
64.4 |
88.0 |
75.6 |
