Agriculture Reference
In-Depth Information
9.5.7
Legume biomass and tissue analysis
We did not collect D1 legume biomass data but determined D2 legume biomass by har-
vesting from a 5 × 5-mm
subplot at the time of incorporation, which was October 2002 and
ran from August to September in 2003 (the timing was changed due to farmers' requests).
Foliar legume tissue samples, collected at time of incorporation, included a composite of
green leafy materials from 12 plants. They were dried, ground, and analyzed for percent-
age N as described previously for maize foliar samples. Legume biomass data were col-
lected from 13 farmers in 2002 and 17 farmers in 2003.
To determine the impact of legume treatments and landscapes, we performed sepa-
rate two-way ANOVAs (*
P
< 0.05) using the following dependent variables: pH, extract-
able P, soil percentage C, soil percentage N, legume biomass, maize foliar percentage N,
maize foliar percentage S, and maize foliar percentage P. Legume treatment and landscape
were the two independent factors. Total N input was calculated by combining organic
legume-based N contribution (legume biomass × legume foliar percentage N), when pres-
ent, and inorganic N quantities. We were unable to transform total N input to achieve
normality or homogeneity of variance and therefore could not perform ANOVAs. Instead,
we performed nonparametric median tests for the independent variable cropping system
(*
P
< 0.05) (Norušis, 2003).
9.5.8
Economic analysis
We performed a distributional cost-benefit analysis for the cropping systems in which we
separately investigated costs and benefits for the wealthiest and poorest farmers because
their marketing strategies varied substantially (see Sirrine, Shennan, Snapp, et al., 2010,
for detailed methods). While wealthier and very impoverished farmers typically sell
proportionally similar quantities of maize (10% of their yields), wealthier farmers often
retain their maize to sell when prices are high, and highly impoverished farmers, in need
of cash after the hungry season, generally sell when prices are low (Center for Regional
Agricultural Trade Expansion Support [RATES], 2003; Peters, 2006). Due to substantial
intra- and interannual fluctuations in costs and benefits (Sirrine, Shennan, Snapp, et al.,
2010), we evaluated profitability separately for the two different design years. We were
unable to present cost-benefit data for middle-income farmers because their marketing
strategies were less well defined. Crop prices and input and labor costs used to estimate
profitability can be found in the work of Sirrine, Shennan, Snapp, et al. (2010).
The methods for our participatory wealth-ranking exercise are described in detail in
the work of Sirrine, Shennan, Snapp, et al. (2010). Briefly, farmers were placed into socio-
economic categories using a participatory wealth-ranking method described and vali-
dated by Adams et al. (1997), in which a few community members helped researchers
place farmers into one of three categories: wealthiest, middle-income bracket, and poorest.
Farmers were placed into these categories based on wealth and vulnerability indicators
specific to the region, including selling their own labor (
ganyu
), hiring casual labor, abil-
ity to afford fertilizer, food availability throughout the year, and landholding size, among
others. Indicators were chosen based on both locally based literature (e.g., Ellis, 1998) and
key community members' perceptions. We later verified whether farmers had been placed
in correct categories through household visits and interviews.
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