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Farm income, gender differentials and climate risk in Cameroon: typology of male and female adaptation options across agroecologies

Sustainability Science volume 6, pages 21–35 (2011)Cite this article

Abstract

This paper explores the response to risk of smallholder agricultural producers in the face of variable and changing climate in Cameroon. The low rainfall distribution in some regions of the country and the high inter-seasonal variability of rainfall makes crop production, on which the livelihood of rural inhabitants is based, a risky enterprise. Women farmers in Cameroon are an important group for whom risk aversion influences production outcomes and welfare. This study identifies and analyses the effect of climate risks on the productive activities and the management options of male and female farmers. Women-owned farms, on average, record profits of US$ 620 per hectare to about US$ 935 for crop enterprises across the different agroecological zones. Comparatively static results indicate that increases in climate variability and the uncertainty of climate conditions have an explicit impact on farm profit. The impacts of increased uncertainty in climate and risk aversion are ambiguous depending on the agroecology. Ex-ante and ex-post risk management options reveal that female-owned farms in the northern Sahel savannah zone rely on more sophisticated strategies to reduce the impact of shocks. While adapting to uncertain climate positively influences profit levels, risk measured as the variance of rainfall or temperature per unit variation in profit is significant. This analysis stresses the increased importance of climate risk management as a prelude to the panoply of adaptation choice in response to expected climatic change.

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Fig. 1
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Notes

  1. Climate variability in this paper refers to seasonal rainfall or temperature variation measured as the deviation of the monthly mean from the seasonal mean of temperature and rainfall as measured in weather stations across the country.

  2. Climate change here refers to change in national precipitation and temperature over time due to variability. It is measured as the statistical deviation of annual seasonal means from the 50-year seasonal mean as measured in regional weather observatories.

  3. The latter effect refers directly to the availability of purchasing power to ensure household welfare. Changes in yields and prices can partly compensate each other.

  4. An individual is said to be risk neutral if the utility of the expected value is equal to the expected utility. In such cases, the individual is said to have a linear utility function. An individual is said to be risk averse if his or her von Neumann–Morgenstern (NM) utility function is strictly concave, i.e. if the utility of the expected value is greater than the expected utility. Similarly, an individual is said to be a risk lover if his or her NM utility function is strictly convex (Takayama 1994).

  5. Since many of the sources of the diversified portfolio of income remain tied to the well being of farming in the community, any shocks that hurt the local agricultural output can place the diversified income of the rural poor in jeopardy. For example, a wide-spread natural disaster (drought or flood) that creates significant yield loss for crops and damages grassing lands for livestock can have a devastating impact on all sources of income. Thus, even a well-diversified portfolio of income for the rural poor may still be vulnerable to a significant covariate risk: natural disasters.

  6. While least square estimation provides a consistent estimate of the parameters of the conditional moments [e.g. in Eqs. 6 and 7], it will be of interest to test hypotheses about these parameters. In general, the conditional moment specifications suggest the presence of heteroscedasticity (e.g. Just and Pope 1979; Yang et al. 1992). This must be taken into consideration in conducting hypothesis testing. We examine this by implementing the procedure proposed by White (1980), which gives consistent estimates of the standard errors in the presence of general heteroscedasticity.

  7. These are the Sahel, Sudan savanna, Low savanna, High savanna (savanna-montane), Forest-savanna ecotone, Guinea savanna, Humid equatorial and Littoral moist equatorial forest. This classification is based on the variability in precipitation, average temperature, vegetation, relative humidity, reference evapotranspiration, wind speed and total solar radiation.

  8. The Artes (Africa Rainfall and Temperature Evaluation System) published by the World Bank in collaboration with climate centers, provides basic statistics of rainfall and temperature for the continent of Africa based on NOAA’s Gridded Africa Rainfall and Temperature Climatological Dataset. Two basic series were provided by NOAA, one is the daily rainfall and temperature from 1 January 1977 to 31 December 2000. The other is the monthly precipitation from January 1948 to December 2001. This is complemented with recent NOAA/FAO satellite information. The precipitation data measures the amount of rainfall in millimetres per unit of area. The temperature is measured in degrees Celsius.

  9. Information on input and output prices with sufficient variation across farms in the different study regions are accounted for in the profit and income estimations. Hence, with producers being price-takers in all the markets, prices are endogenous to the estimation, with market prices and access to the market accounting for profit levels.

  10. To ensure marginal price changes do not mitigate the welfare effects, we compute and use marginal changes in the expected profit. Thus, the marginal impact of a single climate variable, e.g. rainfall RFi, on profit is evaluated on the extent of variation of that variable \( E\left[ {\partial \pi /\delta \sigma_{{{\text{RF}}_{i} }} } \right] \). The obtained marginal change in profit is hence the marginal welfare effect of the change in the variation of the exogenous climate variables. This approach controls for the overestimation of the welfare effects due to price variation.

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Authors and Affiliations

  1. Department of Agricultural Economics and Agribusiness, University of Buea, P.O. Box 63, Buea, Southwest Region, Cameroon

    Ernest L. Molua

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  1. Ernest L. Molua
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Correspondence to Ernest L. Molua.

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Edited by Fukuya Iino, United Nations Industrial Development Organization (UNIDO), Austria.

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Molua, E.L. Farm income, gender differentials and climate risk in Cameroon: typology of male and female adaptation options across agroecologies. Sustain Sci 6, 21–35 (2011). https://doi.org/10.1007/s11625-010-0123-z

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Keywords

  • Cameroon
  • Agriculture
  • Female-owned farm
  • Climate
  • Uncertainty
  • Risk aversion

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