Baseline Survey

The aim of MicroVeg Project is to increase vegetable production while reducing synthetic fertilizer use. Various alternative farming practices have to be considered in order to achieve this objective. There is need to gather data about the livelihood and farming practices in these regions before the actual implementation of the project.
Prior to the actual field survey exercise, the enumerators were trained on data gathering and communication. They were also trained on the use of the software package ODK (Open Data Kit) used in the data gathering exercise.
The baseline study took place in the entire region of south western Nigeria including Kwara, Osun, Oyo, Ogun, Ekiti, Ondo and Lagos State. The whole procedure was replicated in Benin Republic.
The baseline survey took place in the selected region from July to August 2015.

 

Summaries from Baseline Survey

  • Female vegetable farmers are older than their male counterparts with an average age of
    45.65 years and 50.27 years for Benin and Nigeria, respectively. The majority of male and
    female vegetable farmers are middle-aged with an age range of 31-55 years.
  • The level of formal education (88.87%) among female vegetable farmers is higher than the
    level of formal education (80.48%) among male vegetable farmers in Nigeria. In contrast,
    the level of formal education (40.00%) among male vegetable farmers is higher than the
    level of formal education (14.00%) among female vegetable farmers in Benin.
  • For land acquisition, some male (32.08%) and few female (16.17%) farmers lease their
    farm land in Nigeria while very few male (3.10%) and female (0.62%) farmers in Benin
    lease their farm land. Few male (19.94 and 24.84%) and female (12.4 and 11.18%) farmers
    in Nigeria and Benin, respectively, inherited their farm land.
  • In terms of use of fertilizer, vegetable farmers in Benin applied fertilizer at the rate of 129.7
    kg/ha which is above the recommended rate of 112.5kg/ha, while vegetable farmers in
    Nigeria applied 26kg/ha which is below the recommended rate of 80kg/ha.
    With respect to seed sourcing, results showed that seeds saved from last season production
    provided about 51% of the planting material while seeds purchased from the market
    provided 12-32% of the planting material. An exceptionally high percentage (75%) of the
    vegetable producers in Benin purchased their seeds from the market.
  • Vegetable production based on 0.5ha land area resulted in a net benefit of $3,879.00 and
    $3650.00 in Benin and Nigeria, respectively. Benefit cost analysis revealed that in Benin,
    every $1 invested in vegetable production generates a return of about 0.8 cents and 0.3cents
    in Nigeria.
  • In Benin, the total output and total variable cost were 19800kg and $6934.01, respectively
    while in Nigeria, they were 4481.55kg and $2742.96, respectively. Gross profits were
    $1544.48 and $490.23 in Benin and Nigeria, respectively. For every kilogram of vegetable
    marketed, a profit of $0.08 and $0.11 would be expected in Benin and Nigeria, respectively.
  • Vegetable farmers who diversify use the productive resources available to them more
    efficiently. Results showed that farmers who diversify use mostly wetland for their
    operations, especially during the dry season for maximum productivity and profit. In terms
    of fertilizer use, those who plant two UIVs use the most volume (665.27kg of NPK and
    441kg of Urea on 0.5ha of farmland) whereas those who planted all four vegetables used
    the least amount of fertilizer (less than 50%). In the two countries, cultivation of three types
    of vegetables yielded highest income for the farmers.
  • Majority of vegetable farmers with small farm holdings in Benin Republic (100%) and
    Nigeria (67.1%) experience shortage of food for between zero and three months every year.
  • Different forms of business models exist in the UIV value chain. This varies from the use
    of “cartel” in marketing to “contract” farming in production. The particular model engaged
    in depends on the location and the business environment.

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