4.1. Trends in Temperature and Precipitation
An observation of seasonal and annual temperature anomalies shows a steady and significant rising trend between 1960 to 2008 (
Figure 2a). With the exception of an exceptionally low dry season mean temperature in 1989, there is no distinction between the trend of temperature change between the rainy and dry seasons. Unlike temperature, precipitation trends reveal no general long-term seasonal or annual trends (
Figure 2b). The precipitation data reveals incidences of recorded drought episodes that affected Cameroon’s Sudan-Sahel, such as those of 1983–1985, 1987, 1992 and 1997 [
18]. While evident in the data, the more widespread drought that affected large swaths of the African Sudan-Sahel does not leave as big a mark on the time-series as much as more localized drought episodes (
Figure 2b). The precipitation data also reveals greater inter-annual variability in the decade of the time-series.
Figure 2.
Seasonal and annual temperature and rainfall anomalies for Maroua-Salak.
Figure 2.
Seasonal and annual temperature and rainfall anomalies for Maroua-Salak.
Rainfall plays a very important role in agricultural production in the Sudan-Sahel. It has even been estimated that a reduction of about 10% of seasonal rainfall can translate to about a 4.4% decrease in a country’s food production [
19]. Before rainfall translates to food production, however, the rain water is acted upon by a number of environmental processes, such as surface runoff, infiltration and evapotranspiration. These processes depend on a wide-ranging number of environmental factors. Evapotranspiration, for example, which is a considerably important factor of crop production, depends heavily on sunshine, temperatures at the surface of the ground, temperature at the lower atmosphere over the surface, winds and other factors [
20].
Figure 3.
Trend in number of rain days and days with daily rainfall ≥ 40 mm (a) and trends of start of the rainy season and length of dry spells (b).
Figure 3.
Trend in number of rain days and days with daily rainfall ≥ 40 mm (a) and trends of start of the rainy season and length of dry spells (b).
While the general trend of rainfall remains relatively unchanged since the 1960s (
Figure 2), there is a non-significant trend of rainfall falling increasingly unevenly in larger events (
Figure 3a). The number of days of high rainfall also shows a statistically insignificant increasing trend (
Figure 3a). The number of days of rainfall gives an impression of the distribution of rainwater inputs within the cultivation season. The number of days of abnormally high rainfall gives insights on the propensity of floods, soil erosion, the infiltration of rainwater into the soil, crop damage from falling raindrops and winds that usually accompany intense rains in this agro-ecological zone. These are precipitation characteristics that have the potential of affecting crop production in the Sudan-Sahel [
21]. From a farmers’ perspective, besides damaging crops on farms and reducing soil fertility, increasing rainfall intensities can also damage crops during storage at home, as well as limit access to farms for the delivery of farm products. In recent years, studies have reported cases of flood-related damage to crops at different levels of the production and distribution chain, with disastrous outcomes for smallholder farming households and farming communities in Africa’s Sudan-Sahel [
10,
22].
While dry spells are an inherent characteristic of arid and semi-arid climates [
23], research has identified changes in the start of the rainy seasons and probable increases in the length of dry spells as some of the agro-climatic challenges that farmers in the Sudan-Sahel of Africa may have to cope with in the future [
21,
24]. Changes, such as the time of cessation of the rainy season, the length of dry spells and the start of the rainy season, have been reported to be significant in parts of Cameroon [
25,
26,
27]. The data from the study area does not, however, show any significant trends of changes in either the start of the rainy season or the length of dry spells (
Figure 3b). One must note, though, that while the daily data provides a useful understanding of the 25-year period between 1969 and 1994, this analysis would have benefited from better insights if daily data for the last 18 years were available.
4.2. Trends in Crop Production and Area Cultivated
The trend of food crop production is variable for different crops. Maize and cowpeas production has been increasing significantly over the time-series (
Figure 4). The production of all sorghum crops, as well as groundnuts, has remained relatively unchanged, while millet has been falling steadily. The increase in maize production can be attributed to the high demand for the crop, partly fuelled in recent years by industrial demand from breweries. This has made maize production an important activity both for food production and income generation. This has been at the expense of millet, whose net income per unit area cultivated is small compared to maize and whose demand is limited to local consumers. Cowpeas have increasingly been important not only as a locally consumed vegetable, but also as a source of soil nitrogen enrichment for local farming systems. Their importance as a soil enrichment source gained prominence in the early 1990s, when state-subsidized sources of cheap fertilizers were restricted. Sorghum remains a local staple food crop and a base for the manufacture of local beverages, while groundnuts remain the most favorable traditional income-generating crop for small-scale farmers cultivating on sandy soils.
The most important aspect of food production trends, as seen in
Figure 4, is the close relationship between the amounts of area cultivated and total production. The cultivated area for different crops has grown, while production intensity has not. The correlation between total production and total area cultivated of all individual crops is statistically significant at alpha = 0.05 (
Figure 4). Changes in total production over the years are overwhelmingly an outcome of the amount of area cultivated, not the outcome of increases in yields per hectare. In the Sudan-Sahel agro-ecological zone with the highest population density in the country, this trend poses serious challenges for the future of food security. Sustaining food production in the future with increased competition for land (driven by demography and urbanization) may become increasingly more challenging.
4.3. Rainfall Variation and Yields
Notwithstanding the importance of rainfall in food crop production in the Sudan-Sahel, the proportion of variation in crop yields that can be explained by variations in total rainfall amounts (R
2) is generally small (
Table 1). The exception to the limited contribution of rainfall to total yield variation is with sorghum (dry season). This is understandable, because this crop is planted towards the end of the rainy season. It depends on sufficient water falling at the tail end of the rainy season to establish itself. A fall in the total amounts of rainfall during the rainy season, therefore, has the potential of severely affecting the crop [
28]. The correlation (strength of relationship) between crop yields and rainfall is moderate for millet and sorghum and generally weak for the rest of the crops (
Table 1).
Figure 4.
Total production of crops studied (in thousand tons) and total area cultivated (in thousand hectares) 1984–2005 in the Far North region of Cameroon. The data used to develop this figure is derived from FAOSTAT (2012).
Figure 4.
Total production of crops studied (in thousand tons) and total area cultivated (in thousand hectares) 1984–2005 in the Far North region of Cameroon. The data used to develop this figure is derived from FAOSTAT (2012).
Table 1.
Regression of total annual rainfall to yields of major food crops in Cameroon’s Sudan-Sahel.
Table 1.
Regression of total annual rainfall to yields of major food crops in Cameroon’s Sudan-Sahel.
Crop | Statistic |
---|
R-squared | Correlation | Mean Square Error | P-value |
---|
Cowpeas | 0.08 | 0.28 | 0.97 | 0.2125 |
Groundnuts | 0.04 | −0.19 | 1.01 | 0.3846 |
Maize | 0.03 | 0.17 | 1.02 | 0.4327 |
Millet | 0.13 | 0.36 | 0.91 | 0.1116 |
Sorghum (dry) | 0.28 | 0.53 | 0.76 | 0.0132 |
Sorghum (wet) | 0.07 | 0.26 | 0.98 | 0.2515 |
The above results of the regression analysis do not discount the very important role played by rainfall in enabling agriculture and sustaining crop production in the Sudan-Sahel. The low amount of yield variation explained by rainfall on other crops may be associated by the relatively tough crop cultivation conditions of the Sudan-Sahel agro-ecological zone. The severely arid dry seasons means that when the rains begin, considerable amounts of the rain water have to be used up in getting the soil to a moisture level that can support planting. Before the soil reaches optimal levels to permit planting and crop growth, a lot of the precipitation is lost to evaporation and surface run-off on the sparsely vegetated land [
29]. While this portion of the rainfall contributes in building up enabling conditions for cultivation, it does not directly contribute to plant growth, biomass accumulation and yield improvement. Crop pests and diseases are a contributing feature to the tough crop production conditions in this agro-ecological zone [
30] and contribute to yield loses for many food crops. Within the rainy season, agro-climatic events, such as dry spells, can reduce the viability of plant growth and contribute to low yields [
27,
28]. The dominant natural vegetation of this agro-ecological zone is open grassland and sparse shrub land [
29]. As a result, the soil is generally less protected from erosion by wind during the dry season and sheet erosion in the early rainy season. This reduces the quality of the top soil and reduces crop water use efficiency. The quality of seeds for food crops cultivated in this agro-ecological zone has not been breed to fully withstand many of the above constraints. The above constraints point to the important role policies and management play in determining yields or food production in this agro-ecological zone. This can be further demonstrated by an observation of the effects of government intervention on the rainfall-crop yield time-series (
Figure 5).
4.4. Effects of Support for Agriculture on the Rainfall—Crop Yields Time-series
Structural adjustment is a set of measures undertaken with the goal of permitting renewed, or accelerated, economic development by rectifying ‘structural’ disequilibrium in the foreign and public balances [
31]. Between 1960 and 1985, Cameroon's economic growth was based on development of the agricultural sector [
32]. In the late 1980s and 1990s, a number of macro-economic conditions led the government of Cameroon to accept the implementation of the SAPs under the direction of the World Bank and International Monetary Fund. Key among these conditions was a decline in the terms of trade for crop exports, which contributed a large share of the country’s gross domestic product. This situation was aggravated by the fact that most of the income from exports was expressed in US dollars, of which the price against the CFA franc dropped by about 40% after June 1985 [
32]. The result was a slowdown of the economy, with serious deficits arising in public finance and the balance of payments [
32]. The SAP strategy for reforming the economy entailed making it more productive and competitive. With regards to agriculture, the new policy, stressing on liberalization, privatization and diversification, was initiated in the 1990/1991 agricultural season [
32]. On the ground, this meant closing down hitherto state-subsidized companies, agricultural price stabilization schemes and a number of agricultural extension schemes, which provided local level guidance on a wide range of agricultural production enterprises. Through the implementation of the SAPs, small-scale farmers were therefore deprived of a wide range of government-assisted support services offered by agricultural extension workers and technicians on the government payroll. Pre-SAP, the government of Cameroon also provided material support for agriculture through the subsidization of farming inputs, such as fertilizers, seeds and pesticides, as well as proactive policies of soil conservation and agricultural innovation through local, international institutions and extension services [
33]. This support also suffered serious curtailment as a result of the implementation of SAPs [
32]. Hence, the period before the strict implementation of the SAPs (before 1989) was marked by significant support of agriculture at the local level, while the period of strict implementation of the SAPs (1990–1999) saw these support structures and services largely dismantled. Since 2000, the government has been making efforts at supporting agriculture through project-based support packages designed to address specific challenges at different levels of food production [
34,
35].
Figure 5 and
Table 2 present the results of the examination of standardized yields per hectare and total rainfall time-series. In
Figure 5, a clear mismatch exists between total rainfall and crop yields for at least five of the six crops in the period 1984–1989 (the mismatch is less obvious for maize). This mismatch is far less evident for the period 1990–1999 (color-coded in orange). From 2000, the mismatch is not as evident as it is in the 1990–1999 period. The period before 1989 shows standardized yields generally above rainfall. This is the period prior to the introduction of the Structural Adjustment Programmes (SAPs) in Cameroon. During this period, government support for agriculture was proactive and enabled farmers to achieve production levels above those constrained by one of the major biophysical impediments to food production (rainfall) in the region.
Table 1 presents the results of correlation tests between crop yields and periods of different levels of governmental support for agriculture in Cameroon’s Sudan-Sahel. For the period 1984–1989 (when government support to agriculture was still substantial), the strength of correlation between total rainfall and yields is less than that of the period 1990–1999 (when the SAPs were being firmly implemented) for four out of six of the crops. These crops are dry and wet season sorghum, millet and cowpeas (
Table 2). The time-series shows a stronger correlation of rainfall and crop yields during the period 1990–1999 (
Table 2). This suggests that with the absence of governmental support, agriculture tended to be an event-driven system, in this case, driven by rainfall (one of the main variables on which food crop production in this region relies). In the period after 2000, the strong correlation observed in the preceding period (1990–1999) is shown to have been disrupted for four crops (wet and dry season sorghum, millet and cowpeas, in
Table 2). This seems to suggest that emerging governmental efforts at supporting food crop production have the potential of changing agriculture from being a highly event-driven activity to one that draws on an integration of both human and natural resources.
Figure 5.
Standardized total annual rainfall anomalies and standardized yields per hectare for selected food crops in Cameroons Sudan-Sahel agro-ecological zone. The time segments indicating periods with different levels of intervention in agriculture are color-coded: blue = the pre-SAP period (up to 1989), characterized by comprehensive assistance to agriculture; orange = period of the firm implementation of SAP (1990–1999); green = period when the government resumption of assistance to agriculture became more widespread (from 2000).
Figure 5.
Standardized total annual rainfall anomalies and standardized yields per hectare for selected food crops in Cameroons Sudan-Sahel agro-ecological zone. The time segments indicating periods with different levels of intervention in agriculture are color-coded: blue = the pre-SAP period (up to 1989), characterized by comprehensive assistance to agriculture; orange = period of the firm implementation of SAP (1990–1999); green = period when the government resumption of assistance to agriculture became more widespread (from 2000).
Table 2.
Results of test for correlation (r) between total rainfall and crop yields (alpha = 0.05) in the period before the full implementation of SAP (1984–1989), the period of dedicated implementation of SAP (1990–1999) and the period when governmental efforts towards support for agriculture were reinstated (after 2000).
Table 2.
Results of test for correlation (r) between total rainfall and crop yields (alpha = 0.05) in the period before the full implementation of SAP (1984–1989), the period of dedicated implementation of SAP (1990–1999) and the period when governmental efforts towards support for agriculture were reinstated (after 2000).
Crop | Period | Statistic |
---|
r | P-value | R2 |
---|
Cow Peas | 1984–1989 | −0.21 | 0.6868 | 0.045 |
| 1990–1999 | 0.59 | 0.075 | 0.34 |
| 2000–2004 | 0.26 | 0.6701 | 0.069 |
Groundnut | 1984–1989 | −0.73 | 0.1016 | 0.53 |
| 1990–1999 | 0.071 | 0.8463 | 0.005 |
| 2000–2004 | −0.55 | 0.3353 | 0.3 |
Maize | 1984–1989 | −0.4 | 0.4281 | 0.16 |
| 1990–1999 | 0.38 | 0.2785 | 0.14 |
| 2000–2004 | 0.74 | 0.1508 | 0.55 |
Pearl Millet | 1984–1989 | −0.12 | 0.8231 | 0.014 |
| 1990–1999 | 0.73 | 0.0162 | 0.54 |
| 2000–2004 | −0.4 | 0.5071 | 0.16 |
Sorghum (dry) | 1984–1989 | 0.085 | 0.8732 | 0.0072 |
| 1990–1999 | 0.95 | <0.0001 | 0.9 |
| 2000–2004 | 0.38 | 0.532 | 0.14 |
Sorghum (wet) | 1984–1989 | −0.14 | 0.7917 | 0.02 |
| 1990–1999 | 0.74 | 0.0151 | 0.54 |
| 2000–2004 | 0.11 | 0.8626 | 0.012 |
Between 1995 and 2000, the region experienced two major episodes of food price increases, which may have forced the government to rethink the strictness of its implementation of the SAPs. The earliest efforts at revising agricultural support policies and implementing efforts that would increase agricultural production began in the late 1990s [
33,
34,
35]. These efforts have increased over the years. At present, neither all crops nor all agricultural production systems are supported. Some support programs do not even cover the entire country. On the time-series, the effects of these efforts are evident, albeit subtle, since 2000 (
Figure 5). The effect on the time-series is clearer for crops such as wet season sorghum and groundnuts in which support programs are already being implemented in the study area and less clear for millet, cow peas and dry season sorghum, where assistance is limited. It must be noted, too, that it is less clear for maize, even though this crop is already receiving support in the region.
4.5. Climate Extremes, Yield Vulnerability and the Effects of Support for Small-scale Agriculture
Between 1984 and 2004, the study region experienced several years of drought (
Figure 6). Some of the drought episodes, as well as their effects on regional hydrology and livelihoods, have been recorded and reported [
18,
25]. A correlation analysis computed for mean standardized crop yields on standardized PDSI over the entire time-series (1984–2004) gives a correlation of 0.38. This indicates that the association of yields to droughts in the correlation model is 38%. By comparing yields over the time-series with a history of state intervention in agriculture, one again finds that during the years of government support to agriculture (1984–1989), drought had less impact on yields than in years of less or no support (from 1990–1999). The correlation between PDSI and the mean of crop yields is statistically significant for the period 1990–1999 and is not for the period before or after this (
Figure 6). In the absence of support, yields are therefore strongly associated to climatic shocks, such as droughts.
Figure 6.
Drought incidences 1984–2004 and their correlation with the mean of standardized yields in Cameroon’s Sudan Sahel. Data for the figures is derived from Dai (2011).
Figure 6.
Drought incidences 1984–2004 and their correlation with the mean of standardized yields in Cameroon’s Sudan Sahel. Data for the figures is derived from Dai (2011).
In the 1984 drought, farmers and the government were caught off-guard by unusually long periods of dry spells, which destroyed crops and yields that were already planted. In the time-series, the yields in 1984 were affected notwithstanding the existence of government support schemes (
Figure 6). In subsequent drought years (up to 1989) when support for agriculture was sound, government response was instrumental in shielding yields from the effects of droughts. Support took the form of more comprehensive advice on planting dates through meteorological forecasts, advice on appropriate locations for cultivating specific crops and the usual support in subsidized seeds, fertilizer, pesticides and farming credit. As a result, standardized yields remained well above PDSI throughout most of the drought years (
Figure 6). During the period of devoted implementation of the SAPs in 1990–2000, the effects of the droughts of 1990, 1992 and 1997 are more evident on crop yields. The decline in crop yields and resulting food production resulting from these droughts may be explained by the heavy dependence of farmers on nature (rainfall in this case) with little or no cushioning for climatic shocks. Such heavy dependence on rainfall in sensitive agro-ecological zones, such as the Sudan-Sahel, leaves farmers vulnerable and threatens food security.