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Returns to Fertilizer Use for Smallholder Farmers in Kenya

Development Challenge

By some estimates, approximately 1.4 billion people live on less than $1.25 a day1, and many of these are farmers. Identifying ways to increase agricultural incomes is crucial to alleviating poverty. Such strategies are especially important in sub-Saharan Africa, a region in which agricultural yields have been low and remained stagnant for many years.


An estimated 66% of the population of Kenya’s Western Province lives below the poverty line, which often means they are unable to afford enough food to meet their basic calorie requirements as well as their non-food needs.2 The majority of subsistence farmers there grow maize as their staple crop, but many have only small amounts of land and are actually net buyers of maize, purchasing it when their own supply runs out immediately before a harvest. In such an environment, improving agricultural productivity could have substantial benefit on their livelihoods. An important input into increasing productivity is chemical fertilizer. Numerous agricultural trials on experimental farms suggest substantial returns to fertilizer, and improved fertilizer use has been associated with the increase in agricultural incomes during the Green Revolution in South Asia. However, only 40% of sampled farmers in the Busia district of Western Kenya report ever having used fertilizer.

The overall goal of this research program is to understand why farmers do not invest in fertilizer. This part of the project first investigates whether the returns to fertilizer are actually substantial on real-world farms in real conditions.

Evaluation Strategy

In collaboration with International Child Support (ICS), an NGO, researchers set out to experimentally measure the returns to fertilizer among area farmers.  Farmers were selected from lists of parents at local schools, and ICS paid for fertilizer and hybrid seeds, delivered materials, helped these farmers apply fertilizer and seeds, and assisted them with the harvest. On each farm, a comparison plot was kept directly next to treatment plots, which was farmed using traditional methods. The type of seed and amount of fertilizer applied to each plant was varied by plot (see below), but farmers were instructed to tend all plots exactly the same.


Fertilizer/Seed Combination

Time of Application

# of Plots


¼ tsp Calcium Ammonium Nitrate

2 months after planting



½ tsp Calcium Ammonium Nitrate

2 months after planting



1 tsp Calcium Ammonium Nitrate

2 months after planting



Hybrid Seeds

1 tsp Di-Ammonium Phosphate

1 tsp Calcium Ammonium Nitrate

(the “full package” recommended by the Kenyan Ministry of Agriculture)

At planting

At planting

2 months after planting


Results and Policy Implications

Impact of Crop Yield: All fertilizer treatments led to increases in yield, though in different amounts.  Interventions A, B, and C led to yield increases of 28%, 48% and 63% respectively, relative to comparison plots.  Intervention D, the Ministry of Agriculture recommended package, led to an average 91% increase in yield relative to comparison plots.

Rates of Return: On an annualized basis, interventions A and B had positive returns of 8.4% and 69.5% respectively. Interventions C and D had negative rates of return at -17.8% and -48.2% respectively.3 This evidence demonstrates that fertilizer use can have substantial returns, even in the absence of any changes in other farming practices, on real-world farms. However, returns to incorrect quantities of fertilizer yield much lower, even negative returns, and government information on the appropriate use of fertilizer may maximize yield, it was not profitable and may not be appropriate in this case.



1Shahua Chen and Martin Ravallion (2008). “The Developing World Is Poorer Than We Thought, But No Less Successful in the Fight against Poverty,” World Bank Policy Research Working Paper #4703.

2National Coordinating Agency for Population and Development (NCAPD) [Kenya], Ministry of Health (MOH), Central Bureau of Statistics (CBS), ORC Macro. 2005. “Kenya Service Provision Assessment Survey 2004”. Nairobi, Kenya: National Coordinating Agency for Population and Development.

3Note that these estimates do not account for differences in labor time across the 2 plots. For a fuller set of profitability calculations, see Duflo et al. (2010).