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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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For the 2012 harvest, NK M&E collected baseline data for both new Nuru farmers (intervention group) and non-Nuru farmers (comparison group) of the 2011 LR yields. In the 2013 and 2014 growing seasons, the yield data were collected after harvest to assess the impact of NK Agriculture.

For maize trade in Kuria West, Kenya, 90 kg bags are the standard unit of measurement for buying and selling maize.

Data collection: May 26-June 13, 2014 • Data entry and quality control: May 27-June 18, 2014 • Analysis Following data entry, the data were analyzed using descriptive statistics by way of analytical tools such as Stata and MS Excel. Outliers, specifically farmers who reported that their overall yield was more than 3,483 kgs per acre, were scaled out of the data set; such yields are not feasible according to maximum possible yields for specific seed varieties as reported by local seed companies. NK Agriculture collected additional information regarding MLND and crop loss, which was stored in MS Excel and used to triangulate survey findings.

Each plot of land farmed with Nuru was calculated via a manual pacing method; enumerators determined the shape of the field (e.g. rectangle) and then walked the length and width of the field, where one pace equals two steps7. Generally, individuals walk a different distance in one pace. To convert from paces to meters, enumerators paced a measured distance of 100 meters three times. Each enumerator calculated his/her average paces per meter, which became the means to convert paces to meters.

For Nuru farmers who selected to plant the diversified loan package, M&E converted the corresponding yields of sorghum and millet to the equivalent market value of maize using a crop equivalent yield method; see section below for further information on these calculations.

M&E collected additional data about the farmers, including age, gender, education level, seed varieties planted, soil type, land drainage (water table), and climate information. M&E also collected information on alternate crop cultivation and market accessibility for each household. The following sections will cover the calculations of Crop Equivalent Yield (CEY), Food Security, and Agricultural Income.

Approach to Calculating Crop Equivalent Yield Under the monocropping strategy, the evaluation methodology compared maize yields between Nuru and non-Nuru farmers. The units were the same, kgs of maize per acre, which made comparison straightforward. The diversified crop strategy introduces two additional crops to evaluate, which complicates the methodology. To ease understanding and use of this data, M&E developed one composite picture of crop performance. The crop equivalent yield calculation converts the performance of a portfolio of different crops to one standard unit to create a composite index of crop performance.

M&E used the farm gate prices of finger millet, brown sorghum, and maize to convert the performance (yield per acre) of three crops to a single metric, yield per acre of maize. The input Land farmed with Nuru is typically a subset of total household farmland.

data used were the yield per acre and the farm gate price of each crop. Then, M&E applied the

following equation:

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One way to interpret this formula is to ask: If you sold all of your finger millet, sorghum, and maize, and then bought only maize, how much maize could you buy? The formula accounts for average agricultural production (kg) of each particular land area (acres). In other words, it calculates the value of a portfolio of crops per land area. This value is then expressed in terms of kilograms of maize per land area. This formula accounts for crops like finger millet, which tend to produce a lower weight of more valuable produce per land area.

Approach to Calculating Food Security (Household Hunger Variables) The Household Hunger Survey (HHS) is an evaluative tool designed by the Food and Nutrition Technical Assistance (FANTA) and USAID in 2006 to assess the validity of the Household Food Insecurity Access Scale (HFIAS) in a cross-cultural context8. The HHS authors recommend applying the tool during or directly after the worst of the lean [hunger] season where the tool measures the prevalence of food deprivation, and subsequently applying it at the same time of year each year to measure household food insecurity between years (Ballard, et al., p. 6). M&E has essentially stacked these two timing strategies and their respective purposes by assessing households during the hunger season at the same point in time each year. The scale thus captures farmers’ experiences of hunger after the previous harvest, which can be framed as a cycle of household hunger where there are times of food surplus and food shortage for a household between the two annual harvest seasons.

HHS focuses on three areas related to hunger and food security. They include:

1. Anxiety about food supply, including the ability to purchase or barter for food;

2. Insufficient quality, including access to different varieties, preferences, and socially acceptable foods in the community;

3. Insufficient supply, intake, and consequences.

In June 2013, NK M&E adapted the HHS survey to conduct baseline household hunger scores for 656 households in two divisions of Kuria West with two foci. First, NK M&E wanted to assess the overall incidence of hunger in the community among new and returning Nuru farmers. Second, in order to assess one of the long-term goals of NK Agriculture, e.g. “[f]armers [who] experience Ballard, Terri; Coates, Jennifer; Swindale, Anne; and Deitchler, Megan. Household Hunger Scale: Indicator Definition and Measurement Guide. Washington, DC: FANTA-2 Bridge, FHI 360.

increased maize yields, experience less or no hunger during traditional hunger season periods9”, NK M&E split the sample of households between new Nuru farmers and returning Nuru farmers for the 2013 hunger season. Such a split allowed for better comparison between returning Nuru farmers who harvested maize with Nuru during the 2012 long rains season and new Nuru farmers who did not work with Nuru until 2013. NK M&E repeated this process in 2014 following the same approach to analysis. During 2014, the non-Nuru farmers who had not participated with Nuru previously became Nuru farmers in the 2014 season and were then assessed for this survey.

The survey methodology is implemented through a series of questions aimed at accurately gauging the occurrences of hunger during the traditional hunger seasons of each respective community.

Surveys are conducted at the height of hunger seasons to reduce potential sampling error related to issues surrounding recall. As such, questions are framed in a manner requiring respondents to recall matters related to the experience of hunger within the past four weeks or 30 days at the time of the survey. The survey was disseminated to new and returning Nuru farmers in order to gauge the difference in perception of hunger between the two groups.

NK M&E tabulated respondent answers across three main questions to establish a household hunger score according to the HHS. HHS ranks scores from 1-6. This scale was then consolidated as follows: scores ranging from 0-1 are classified as No Hunger, while scores ranging from 2-6 indicate Low to Severe Hunger. Food secure households are households with scores of 0-1.

Approach to Calculating Agricultural Income In 2012, M&E developed a methodology to determine the overall increase in revenue and agricultural income for Nuru farmers due to NK Agriculture. Changes in agricultural income are measured only for the particular crop production farmers engage in as part of the Nuru loan package. The measurements are based upon the theory developed from farm gross marginal analysis, a tool for planning agricultural investments. Its use here takes into account costs in terms of inputs only 10 (while excluding labor and land opportunity costs to farmers before and after farming with Nuru). This approach aims to represent the production costs Nuru and non-Nuru farmers incur from one season of cultivation in order to calculate the average agricultural income gains of farming with Nuru.

These calculations are generated by comparing the income11 of farming with Nuru Kenya with the income of farming maize without the intervention. Both costs and revenues are adjusted to the common unit of Kenyan shillings (KSh). Costs are calculated by totaling the cost of inputs (e.g.

fertilizer, seed) for non-Nuru farmers and the cost of inputs incurred by Nuru farmers. Revenues are calculated by converting Nuru farmers' yield of maize, millet, and/or sorghum, and non-Nuru farmers' yield of maize, to KSh. The total revenue for households is calculated by subtracting the This quote comes from the NK Agriculture Logic Model. Traditional hunger seasons in Kenya span from May-July and again from November-December. NK M&E surveys households during the May-July hunger season.

Gross Marginal Analysis Tools. Retrieved from http://dpipwe.tas.gov.au/agriculture/investing-in-irrigation/farm-business-planning-tools on 18 November 2014.

Income = Total Revenue – Total Costs Total Revenue (KSh) 12 from the Total Costs (KSh) 13 to produce the Net Profit (KSh) 14. This methodology is comparable to other organizations, such as One Acre Fund, that also measure overall increase in revenue and agricultural income.

Results 2014 Crop Equivalent Yield (CEY) Results15 The 2014 loan package included inputs for maize, brown sorghum, and finger millet. Since its inception, NK Agriculture has helped Nuru farmers to intensify maize production at the household level. Typical households in Kenya rely on maize for the majority of their calories and as a staple food for up to two or three meals a day. The majority of rural Kenyans perceive food security as equivalent to maize availability. Surplus maize production can be readily sold at market. However, the susceptibility of maize to droughts and crop diseases, particularly with the rise of MLND in Kenya since late 2011, motivated NK Agriculture to switch to a crop diversification strategy.

Sorghum and finger millet are nutritious, afford opportunities for strong return on investment, and are much more tolerant to drought, disease, and pests. While sorghum and millet present clear benefits, there are also challenges with farmer adoption. These particular crops are perceived as traditional or backward, especially by younger generations of farmers. In addition, these crops are rarely planted with best agricultural practices and correct fertilizer regimens, furthering popular misconceptions that they are not productive or economically viable. In order to mitigate these perceptions in the field, NK Agriculture launched a campaign of messaging, training, and informational materials, often showcasing lead farmers as examples of successful early adopters to the rest of the community.

As described earlier in this paper, the 2014 loan package provided for one acre of production, including 0.5 acres of maize inputs, 0.25 acres of brown sorghum inputs, and 0.25 acres of finger millet inputs. Each farmer took a loan package for either one or two acres of land. In total, 4,318 farmers took 4,614 acres-worth of input loans. Participating farmers attended technical training to learn best agricultural practices, received extension services, and relied on group work for mutual support.

Among a representative sample of 407 Nuru farmers and 476 non-Nuru farmers, Nuru farmers who adopted the full-diversified crop strategy had an advantage over non-Nuru farmers who planted maize only: 765 kg per acre compared to 693 kg per acre, respectively16 (Table 3).

Revenues = yield * sales price Costs = sum of cost of inputs Net Profit (or "net income") = revenue - costs Data sources and analyses files are available upon request.

While all farmers who farmed with Nuru in 2014 signed on to a diversified crop scheme, a limited number of farmers adopted the full (maize-sorghum-millet) loan package as originally intended. Therefore, improving adoption of crops and best practices was identified as a key activity for the 2015 planting season.

When compared to previous years, where NK Agriculture implemented a monoculture strategy, the 2014 year analysis (Table 3) indicates that Nuru farmers who adopted the full diversified loan package harvested 242 kg per acre more (+46 percent yield gain) than the yield they harvested in 2011, the baseline harvest year.

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Nuru farmers who planted the diversified loan package increased yield more than non-Nuru maize only farmers. However, only one-third (30 percent) of Nuru farmers adopted the diversified loan package. The majority planted maize only (50 percent) or a combination of maize plus sorghum and/or millet (20 percent). See Appendix A for additional information regarding Crop Equivalent Yield (CEY) results for 2014.

Relative to the non-Nuru farmer group, there were also more reports of crop loss, possibly caused by MLND. For the 2014 long rains season, MLND did not affect all divisions equally; this is reflected in the variations in reported crop loss and harvest yields. For example, among Nuru farmers who reported high rates of crop loss, NK M&E recorded production as low as 558 kg per acre; whereas in areas where there were lower reports of crop loss, production reached as high as 1,053 kg per acre. Due to the fact that maize comprises 50 percent of the loan package for Nuru farmers, these discrepancies among Nuru divisions indicate that the advantage between Nuru and non-Nuru households could have been greater in the absence of MLND. See Appendix B for additional information regarding crop loss linked to MLND.

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