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«December 2014 Authored by Bethany Paris With contributions from Kim Do Kristin Lindell Veronica Olazabal Executive Summary The Nuru Kenya (NK) ...»

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2014 Nuru Kenya Agriculture Program

Impact Assessment

Results of the 2014 Agriculture Harvest Yield Survey

December 2014

Authored by

Bethany Paris

With contributions from

Kim Do

Kristin Lindell

Veronica Olazabal

Executive Summary

The Nuru Kenya (NK) Agriculture Program aims to impact crop yield, food security, and household

income by providing member farmers with a farm input loan, technical training, extension services,

and group support structures. The Nuru Monitoring and Evaluation (M&E) team supports this work by conducting annual reviews of progress toward the program’s impact goals to address the evaluation question: What is the impact of the Nuru Kenya Agriculture Program?

The impact of NK Agriculture is determined by measuring three different indicators related to crop yields, food security, and agriculture income. Building off of similar surveys in 2012 and 2013, M&E collected harvest yield data in September 2014 from a representative sample of Nuru and nonNuru farmers. M&E also implemented a follow-up household food security survey during May and June 2014 in order to capture farmers’ perceptions of hunger relative to non-Nuru farmers. Data for the income model came from households who participated in the harvest yield survey as well as from national market prices.

In 2014, NK Agriculture implemented its first ever diversified crop strategy by offering maize, finger millet, and brown sorghum inputs to farmers for planting during the long rains season, which is one of two planting seasons in Kenya. This strategy aimed to increase farmer resilience to insect damage, crop diseases like Maize Lethal Necrosis Disease (MLND), and environmental aberrations, such as the drought which particularly affected Nuru farmers in 2013. With the introduction of the diversified crop strategy, analysis performed on the household level data collected was adjusted to answer to the following questions: What is the impact of a diversified crop strategy and how does it compare to a monoculture approach? Given the need to compare the production of three different crops in 2014 to the production of a single crop in previous years, M&E used a crop equivalent yield (CEY) approach to track progress over time—essentially converting this season’s production of sorghum and millet into “maize equivalent units.” Similar to previous year assessments, M&E measured household hunger levels using USAID’s Household Hunger Scale1 and calculated agricultural income based on the theory developed from farm gross marginal analysis, which is a tool utilized for planning agricultural investments.

The results of the crop harvest yield data in 2014 indicate that Nuru farmers who successfully adopted the diversified loan package of maize, sorghum, and millet produced more than their “maize-only” counterparts—approximately 10 percent more kgs per acre. Interestingly, when looking at the data over time since 2011, this net difference increases to 36 percent. It confirms that a diversified strategy is a stronger approach for Nuru farmers, especially given the uncertainty faced regarding rain patterns and crop diseases. In terms of agricultural income, the 2014 harvest resulted in a 30 percent increase in revenue over the baseline value collected in 2011, which represents a 4 percent increase in profit from the loan package.

Ballard, Terri; Coates, Jennifer; Swindale, Anne; and Deitchler, Megan. Household Hunger Scale: Indicator Definition and Measurement Guide. Washington, DC: FANTA-2 Bridge, FHI 360.

The 2014 results are a conservative account of the diversified loan package’s impact given that a majority of Nuru farmers (70 percent) did not plant the combination of all three crops in tandem during the long rains season as recommended. Among these farmers, some chose not to plant either millet and/or sorghum, saved the inputs to plant in a successive season, or otherwise did not plant all three crops for another reason. NK Agriculture will continue to confront the challenge of changing farmer behavior to adopt crop diversification, where an initial cohort of early adopters in 2014 long rains experienced success with sorghum and finger millet, which will help contribute to other farmers adopting this positive behavior. It is a process that will take time and effort, which includes revamping crop training especially for planting, improved marketing strategies, and reducing the crop offering to either sorghum or millet in 2015, which will reduce the barrier of adopting crop diversification for farmers. There was also incidence of MLND in 2013 which exacerbated results given that many farmers relied on a maize-only approach in both 2013 and 2014.

The analysis for food security indicates that Nuru farmers who have farmed with Nuru over time are more food secure than farmers who have not: 52 percent compared with 47 percent, respectively.

Even though farmers experienced decreases in food security relative to 2013, it is important to highlight the cyclical nature of the hunger and harvest seasons. Food security data are captured during the hunger season, which follows the previous year’s harvest. As 2013 was an extreme drought year, the expectation was that farmers surveyed in May and June 2014 would be less food secure. While Nuru farmers reported being more food secure than non-Nuru farmers, the 2014 data confirm that farmers suffered more during this hunger season compared to past hungers seasons due to the poor 2013 harvest.

In conclusion, the evidence demonstrates that Nuru farmers are steadily increasing their crop yields and reporting a decrease in household hunger when compared to non-Nuru farmers.

Recommendations for NK M&E and Agriculture to consider as a result of these findings are as


1. Promote the diversified crop strategy as a long-term solution for Nuru farmers with the caveat that conforming to the prescribed crops and agricultural best practices are a must to maximize the success at the household level.

2. Increase training, supervision, and monitoring of farmers during ground preparation and planting in order to guarantee the proper use of inputs, timely planting, and correct crop spacing.

3. Continue a proactive strategy of avoiding, preventing, detecting, and eliminating MLND by promoting a diversified crop strategy and raising farmer awareness of the disease.

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Introduction The Nuru Monitoring and Evaluation (M&E) Program produces useful and relevant information that can contribute to key decision-making about Nuru’s programs (e.g., whether to continue, replicate and/or scale an intervention, etc.). With this focus on utilization at the center of its strategy, the M&E team works to objectively monitor and evaluate the performance and impact of Nuru’s four impact programs—Agriculture, Financial Inclusion, Healthcare, and Education.

In service to this approach, the Nuru Kenya (NK) M&E team administered a household level survey in September 2014 that built on a similar data collection in 2011, 2012, and 2013 and aimed to answer the questions: What is the impact of a diversified crop strategy and how does it compare to a monoculture approach? This paper addresses this question, reviews M&E’s approach to assessing the impact of the program, and highlights the findings for crop yields, food security, and income gains.

The Integrated Nuru Model Nuru International is on a mission to end extreme poverty in remote, rural areas. Communities facing extreme poverty deal with fundamental challenges around hunger; an inability to cope with economic shocks; averting preventable disease and death; and illiteracy. Nuru has proven its ability to deliver lasting impact in these four areas and is currently positioning its model for global scale.

As a catalyst for sustainable development, Nuru’s role is to identify nationals to raise up as servant leaders and nation builders; remove barriers preventing them from realizing their full potential;

equip them with skills, resources, and attitudes to end extreme poverty in their region; and build social enterprises to provide a reliable, market-based source of capital.

By establishing community development organizations that are locally led and also launching social enterprises to fund the work, Nuru enables nationals to lift an entire region out of extreme poverty within seven years.

Nuru Kenya Agriculture Program The Nuru Kenya (NK) Agriculture Program provides the farmer a complete agricultural package: a farm input loan, technical training, extension services, and group support structures. At harvest time, farmers finish repaying their loans and commercialize their surplus produce with the assistance of Nuru Kenya.

During the 2014 long rains (LR) season, NK Agriculture offered a diversified loan package for the production of 0.5 acres of maize, 0.25 acres of brown sorghum, and 0.25 acres of finger millet. The inputs included improved hybrid seed for each of these crops as well as planting fertilizer (DAP) and top-dressing fertilizer (CAN). For the purposes of this analysis, non-Nuru farmers were assumed to plant only maize.

Due to low yields in 2013 resulting from drought, NK Agriculture shifted from a monocropping strategy to a diversified crop strategy in 2014. Planting a variety of crops insulates farmers from climate, pest, and disease related risks. In Kenya, where periodic drought is commonplace and Maize Lethal Necrosis Disease (MLND) has threatened maize cultivation nationwide since 2011, crop diversification is an indispensable part of prudent agricultural investment for Nuru Kenya and smallholder farmers alike. However, the crop diversification strategy is not without its drawbacks and challenges, foremost of which is changing behavior of smallholder farmers to invest in crops besides maize and adopt other resilience-building strategies. This was a challenge during the 2014 planting season.

Methodology The following outlines this year’s methodology for selecting the sample population to assess the three core indicators of the program: 1) crop yield; 2) food security; and 3) agriculture income.

Sampling Frame In 2014, 467 Nuru farmers were randomly selected from the pool of 4,318 Nuru farmers in Kuria West district. These farmers live and work in Isibania, Kehancha, Masaba, Mabera, and Ikerege divisions. See Figure 1 (below) for the location of these divisions.

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As in previous years, the final sampling frame was geographically and randomly stratified by division and then sublocation to ensure that a sufficient number of farmers were represented in each stratum for comparison across categories. For the 2014 survey, all five divisions (Isibania, Kehancha, Masaba, Mabera, Ikerege) were proportionally represented in 13 out of the 18 sublocations2 where NK Agriculture works (Table 1).

Table 1: Number of Nuru Farmers Surveyed in the Intervention Sublocations by Year3

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Some sublocations in Salesforce have been further divided by NK Agriculture to facilitate operations in areas largely populated with Nuru famers. M&E used the 20 locations tracked in Salesforce to create the sampling frame.

During the 2014 harvest, the random sample included farmers from Isibania, Mabera, Masangora, Nyanchago, Nyangoge, and Nyankore, areas which were not included in previous analyses. In addition, Komosoko, Ngochoni, Nyamotambe, and Nyangiti were not included in the 2014 analysis.

A non-Nuru farmer group, comprised of 506 farmers, was randomly selected from Masaba division as in previous years. The sampling frame for the 2014 comparison group was calculated in the same manner as previous years (2012-13) in order to collect a statistically significant sample of the villages4 through a stratified random sampling technique (Table 2).

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Village population data were retrieved from the 2009 Kenya Census data collected by the Kenya National Bureau of Statistics, Population and Housing Census. (http://www.knbs.or.ke/index.php) Before finalizing the samples, an additional 10 percent was added to both the Nuru and non-Nuru farmer samples to account for absenteeism and potential outliers. Post-analysis, both the Nuru and non-Nuru samples were adjusted to exclude farmers with missing data points and farmers with unrealistic yield results (i.e. outliers) during the analysis phase; these adjustments reduced the total number of farmers surveyed down to 407 Nuru farmers and 476 non-Nuru farmers.

Data Collection5 In 2014, NK M&E officers supervised the data collection process and contracted 16 enumerators in place of field managers to collect the data in the field. Data were collected from farmers to calculate the number of kilograms (kgs) harvested per acre6. As in previous years, farmers selfreported the number of bags they harvested in 2014. Yield data are collected through recall, which means that farmers report data based on what they remember harvesting.

To ensure the quality of the data analyzed in this report, NK M&E built a system of checks and balances into the data entry process whereby each individual survey was reviewed three separate times before final entry. First, NK M&E closely supervised the data entry process by constantly reviewing for common errors. Throughout the process, data entry clerks highlight systematic data collection errors so that supervisors can correct any field mistakes in real time. Second, surveys were randomly selected for a question-by-question comparison throughout the process. As a final measure, NK M&E randomly called survey respondents from the list of households visited by each enumerator. Conversations with farmers helped NK M&E to understand if enumerators had accurately recorded the farmers’ responses. Consistently poor data collection or data entry resulted in employee termination. Given the system employed by NK M&E, the 2014 season resulted in a limited number of firings as well as exceptional data quality.

Timeline Below is the timeline carried out by NK M&E to implement the NK Agriculture Harvest Yield Survey 2014:

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In addition to the agriculture survey, M&E surveyed a total of 628 farmers using the Household Hunger Survey, which included 311 “new” farmers and 317 “returning” farmers. M&E selected and

trained 12 enumerators according to the following timeline:

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