addressing soil fertility and food security issues with fertilizer trees in malawi

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Addressing soil fertility and food security issues with fertilizer trees in Malawi Richard Coe, Joyce Njoloma, Fergus Sinclair, Bruce Sosola, Isaac Nyoka 14 April, 2015 BICC Beating Famine Southern Africa Conference

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Addressing soil fertility and food security issues with fertilizer trees in

Malawi

Richard Coe, Joyce Njoloma, Fergus Sinclair, Bruce Sosola, Isaac Nyoka

14 April, 2015 BICC

Beating Famine Southern Africa Conference

Presentation outline

Introduction

Methodology

Results and discussion

Conclusion

Acknowledgement

Introduction

• Agroforestry technologies provide

the services and production functions oftrees

• Contribute to solutions on the declining soilphysical, chemical, biological characteristicsand qualities.

impacts on soil health

• affect the soil productivity

• subsequent food security

Introduction

o Agroforestry for Food Security Project (AFSP) started in 2007

o Aim of bringing benefits of agroforestry to large numbers of farms in Malawi,

o Through development of input channels, policy and capacity.

o Followed conventional model

o a theoretical basis

o agricultural experimentation by researchers,

o adaptive research ..similar results from elsewhere and small pilot projects with farmers

Methodology

• Study period 2011/12 and 2012/13 cropping seasons,

• Questionnaire administered to the household heads

• A 6m x 6m quadrat was marked in the agroforestry and non-agroforestry plots.

• Maize yield was measured in the quadrats.

Promoted trees soil fertility technologies

Interplanting maize with

Faidherbia albida

Gliricidia sepium

Annual undersowing with

Tephrosia vogelii

Cajanus cajan (pigeon pea).

Rotational with

Sesbania sesban

Data analysis• Data analysis was based on the difference in maize

yield of the two quadrats

• Yield difference = yield on agroforestry system plot –yield on sole maize plot in the same season.

• Statistical analysis used descriptive statistics and regression analysis of yield difference on plot and farm-level explanatory variables.

Data analysis

• When the two quadrats (with and without

agroforestry) measured on the same farm had

different levels of an explanatory variable, then

linear mixed models that included a random

effect for farms was done.

• All statistical analyses were done using R (R

Core Team 2014).

Results and Discussion

Cropping system Mean maize yield (t ha-1

)

2011/12 season 2012/13 season

Gliricidia sepium 3.62 3.62

Faidherbia albida 3.06 3.13

Cajanus cajan 3.12 2.97

Tephrosia vogelii 3.09 3.35

Sesbania sesban 3.10 ----

Control fields (no

trees)

2.73 2.78

Table 1. Overall mean yields of maize grain in

agroforestry and matching sole crop plots.

Figure 1: Distribution of effects ofintercropping gliricidia on maize. Our data(‘farms’) compared with other publishedresults. See text for details

Means effects quoted in these three papers by (Akinnifesi et al. 2006; Akinnifesi et al. 2007; Akinnifesi et al. 2010). These are from experiments on maize and gliricidia in Malawi.

A survey similar to this one (Akinnifesi et al. 2009).

Results of a meta-analysis compiling data from all over Africa and other tree species managed in a similar way to the gliricidia (Sileshi et al. 2008).

Fertilizer on control

yes no

Fertilizer on

gliricidia

yes 0.64 (51) 2.46 (3)

no 0.04 (21) 1.31 (9)

Table 2. Effects of gliricidia. Mean maize

yield difference (gliricidia - sole) (tha-1) for

plots with and without fertilizer. Number of

pairs in parentheses.

Difference, d, between maize

with gliricidia and sole maize

CaseElevatio

n (m)

Trees per

plot

Fertilizer

applied mean

Probability

d>2

Probability

d<0

1 500 10 Both 1.3 0.32 0.22

2 1000 10 Both 0.3 0.15 0.42

3 1500 10 Both -0.6 0.05 0.65

4 500 30 Both 1.9 0.47 0.12

5 1000 30 Both 1.0 0.26 0.28

6 1500 30 Both 0.0 0.11 0.50

7 500 10 Sole only 0.6 0.19 0.36

8 1000 10 Sole only -0.3 0.07 0.58

9 1500 10 Sole only -1.3 0.02 0.78

10 500 30 Sole only 1.2 0.32 0.22

11 1000 30 Sole only 0.3 0.15 0.43

12 1500 30 Sole only -0.6 0.05 0.65

Best 500 40 Neither 2.4 0.60 0.07

All contexts sampled 0.6 0.18 0.33

Table 3. Predicted mean increments of maize yield (tha-1)

with inclusion of gliricidia for difference cases, along

with the chance of increases >2 tha-1

and <0.

Figure 2: High within field spatial variability

Homestead: high fertility

Out field: low fertility

Out field: medium fertility

Variation sources

Figure 3: Sources of variation: Diversity of conditions14

Conclusion

• The crop yield performance of theagroforestry options in Malawi studied hereare highly variable.

• The variable performance of new croppingoptions is probably the norm rather thansomething unusual.

• The variability can be divided into two types:

– That explained by contextual factors

– Remaining unpredictable variation.

Conclusion

Acknowledgement

Field team Led by ICRAF Southern Africa Programme Malawi

District offices of Malawi’s Ministry of Agriculture and FoodSecurity in Kasungu, Neno, Ntchisi, and Salima districts,

Landscape management for environmental services,biodiversity conservation and livelihoods Science Domain ofICRAF

Funding support for the study was received the Governmentof Ireland and Science domain Theme on Land Management