Work Package 1. Progress report

Catherine Muthuri, John Nyaga and Philip Smethurst

7th November 2019, Steering committee meeting Nairobi, Kenya

Objective

To enhance knowledge of the impact of tree cover

change on crop productivity, water,

nutrients and livelihoods

1.1 Expand the networks of farmer trials across a range of contexts and establish a long term trial in Uganda

1.2 Collect targeted data on tree-crop interactions and tree products and services from the existing long-term, on-station and farmer

trials and develop suitability maps for different sites and contexts

1.3 Develop the capacity of the APSIM X AF model and other linked models for use on a wider range of tree species and cropping

practices.

1.4 Conduct scenario modelling using the enhanced APSIM X AF model and other linked models to inform national agroforestry

scaling

Activities

Region Type of participatory trials Total

farmersTotal trials

Trails

monitored in

ODK

EEFRI / semi-

aridFruit tree trial 182 260 61

MPT planting 380 383 37

OARI/ sub

humidFruit tree and MPT trial 131 287

WVE / Tigray

Fruit tree and MPT trial 70 68

Apple root stock distribution 22 22

MPT with RWH structures 63 133

Fruit tree trial (Guava, Coffee,

and Cazmir )75 75

PhD Student

TigrayFruit tree and MPT trial 20 31

Total 943 1259 98

Participatory Trials Types – Ethiopia Impacts How important do you think products from these trees will be to meeting your household food demand?

Participatory Trials Types – Rwanda Impacts

Types of participatory

trials

Gishwati BugeseraTrials

monitored

using ODKTrials Farmers Trials Farmers

Biomass incorporation 56 232 129 337 187Soil Conservation 114 368 9 23 7Stakes for climbing

beans 81 425 127 129 185Fruits for nutrition and

income (Total) 161 218 630 718 275Tree tomato 138 378 472 531 236Avocado 23 58 77 87 15Mango - 57 65 18Papaya - 24 35 10Total 412 1461 895 1207 658

Type of participatory trial

Mbale Manafwa Bududa Trials monitored

using ODKNo. of trials No. of

farmers

No. of trials No. of

farmers

No. trials No. of

farmersTrees on farm 95 28 85 26 78 35 -Boundary planting 48 35 62 25 73 55 -Woodlots 32 20 21 16 33 28 -Fruit orchards 20 25 14 26 30 15 -Integrated 9 9 24 18 19 31 -

On farm tree management 204 117 206 111 233 164 220

Fodder bank and hedgerows 24 16 18 10 29 13 10

Soil and water conservation 16 16 16 21 12 16 15Riverbank stabilization 22 8 15 6 18 15 10Total 266 157 255 148 292 208 255

Participatory Trials Types – Uganda Impacts

Online data collection progress in Ethiopia, Uganda and Rwanda

Number Name Purpose/objectiveMonitoring using ODK

Ethiopia Rwanda Uganda

1 Farmer profiling/ registration

To profile all farmers hosting participatory

trials. This will allow the project to obtain

contextual variables among the farmers.

214 (943) 176 (1307) 656 (513)

2 Participatory_trial To record and monitor participatory trials. 99 (1259) 658 (1068) 255 (813)

3 RRC_Nursery_Tree_DistributionTo record tree distribution at RRC, numbers

and recipients’ information88 1052 2538

4 RRC_Performance_Checklist

To record the performance of RRC in terms

of visitors, ownership, training, available

resources, satellite nurseries, challenges,

opportunities, and threats.

4 11 3

5 Seedling_Survival_and_Performance

To monitor the survival and performance

of tree seedlings supplied by the project in

the farms.

0 166 554

How did you hear about this RRC? Number of tree species distributed/provided?

No. Activity Outputs/

Milestones

Progress Year 3

Month 10

1.1 Expand the networks of

farmer trials across a range

of contexts and establish a

long term trial in Uganda.

1.1.1 Networks of farmer trials

established: Ethiopia (1500); Rwanda

(1200) Uganda (800)

Rwanda-1307

Ethiopia- 1259

Uganda - 813

Yr1 M12 - Yr

4.

1.1.2a Establishment of LTT Uganda

1.1.2. b) Management trials

established in existing LTTs in Rwanda

and Ethiopia and later Uganda

Done. Monitoring in

progress

Yr1 M6

E&R Yr1 M12

U- MTR

1.2 Collect targeted data on

tree-crop interactions and

tree products and services

from the

1.2.1 Databases developed on crop

productivity and tree products and

services under different agroforestry

practices from the farmers trials

A robust ODK

database was

developed for

Ethiopia, Rwanda

and Uganda. This

process is ongoing,

and the database will

continue to develop

/ enlarge as more

data from the trials is

collected.

Yr1 M10- Yr 4

M6.

Milestone table and summary of progress

No. Activity Outputs/

Milestones

Progress to date.

1.1 Collect targeted

data on tree-crop

interactions and

tree products and

services from the

1.2.2 Databases developed ontree-crop interactions (water, lightnutrients ) and tree and cropproductivity from LTT for use inactivity 1.2.3, 1.2.4, 1.3 and 1.4

Monitoring of trees at long term

trials and established of crops done

in all L.T.T

Yr2 M6 - Yr

4.

1.2.3. Report ona) Achievements and farmeropinions and lessons fromNetworks of farmer trials

1.2.4.b Interim Reports, databases and publications on long termexperiments,

Country-specific reports on participatory trials were prepared for all the countries (Mukularinda et al 2019; Galabuzi et al, 2019a; Gebretsadik et al, 2019a)Lessons on ODK presented at the World Congress on Agroforestry (Nyaga et al 2019a). Lessons from participatory trials in Ethiopia (Derero et al 2019, Kinuthia et al 2019) and Rwanda (Mukuralinda et al 2019b, Cyamweshi et al 2019, Musana et al. 2019) presented at the congress. Manuscripts in progress- Musana et al, Awol et al, Cyamweshi et al.

Yr2 M12

MTR

Objective 1 cont.

• Profiling farmers needs to be finalized to allow analysis of trials performances through option by context approach

• Ensuring T4FS phase 1 trials are also captured and tracking of secondary beneficiaries captured.

• Data collection, documentation and analysis needs to be time especially on participatory trials.

• A plan on write shop to allow drafting of manuscript using the already existing data. Start linking farmer profile data with participatory trials performance data.

Key issues/ outstanding deliverables

Long- term trials

ST4FS Steering Committee: Objective 1 - CSIRO

CSIRO (AUSTRALIA) & ICRAF (EAST AFRICA)

Philip J. Smethurst, Neil Huth, Tenge Ngoga, Athanase Mukuralinda, Fergus Sinclair, and Catherine Muthuri

November 2019

Met and Soil Inputs for Maize Simulations for Rwanda 0.05o Grid• Source data is at 0.25o, leading to blocked appearance for the 0.05o grid. Finer scale source data might be possible later.

• The service sometimes leads to a few cells of missing data. Repeat requests are sometimes successful.

• Highest rainfall and soil C in parts of the west and lowest in parts of the east.

• Soils data need to be compared to national survey results and evaluated for local use, as ISRIC soil modelling and interpolationprobably leads to continental scale smoothing that might lead to with-in country biases in the data. Time of sampling, total soil depth and > 2 mm soil (gravel and rocks) also need consideration. Despite concerns, current data appear useful to some degree– see later slides.

Maize Simulations for Rwanda 0.05o Grid• 794 locations, 34 years, 2 crops/year• N depletion occurring with low N inputs• Observed yields in the project at Tamira (Musana et al.) and Karama (Ngoga et al.) are in the range

simulated after 34 years

0

100

200

300

400

500

600

700

800

2/18/1982 10/28/1995 7/6/2009 3/15/2023

Mai

ze Y

ield

(g/

m2

/har

vest

)

TamiraAPSIMGrid

KaramaAPSIMGrid

TamiraObserved

KaramaObserved

5 kg/ha N at sowing0 kg/ha N at sowing 200 kg/ha N at sowing

Simulated Eucalyptus (7 years)• LAI, biomass, stem diameter and height highest in parts of the west and lowest in

parts of the east

• 5 tree and/or crop zones• 34 years historical met• 2 crops per season• 749 grid points• 247,235 simulations• 31 hours on a laptop• database file 3.7 GB

• Higher yield potential in the east (warmer)

• East also has lowest relative yield under gliricidia (drier and lower soil N), i.e. highest maize suppression by gliricidia

Maize yield in the crop-only zone Relative maize yield in the tree zone

Gliricidia-maize simulations (agroforestry treeproxy)

0

100

200

300

400

500

600

700

0 100 200 300 400 500 600 700 800

Growing Season Rainfall (mm)

Bugesera, Rwanda

Maize Flowering

Maize Population

Tree Row Spacing

Tree Water Use

Mai

ze G

rain

Yie

ld (

g m

-2)

• Experimental results in dry years may not be representative of most other years

• We underestimate the value of leguminous trees if we don’t maximise Maize agronomy

• Competition from trees more likely to be via impacts on crop establishment

• The main drivers are things that farmers can manage

• Modern analytical techniques allow us to look past the complexity to see just the things that we need to see.

• Looking for trends in the things that

matter tells us what we probably should be looking into next.

• See Huth et al. WCA presentationhttps://agroforestry2019.cirad.fr/FichiersComplementaires/webconf/5_39_HUTH%20Neil/index.html

Gliricidia-Maize (active tree model) - Yield Sensitivity

• Maize yield was suppressed up to 1.5 m away from 20-month-old coppiced gliricidia

• Stake production per gliricidia tree was 50% less in the sole-tree treatment

• Stake length (tree height) was 2.5 m on 36-month-old coppice, which increased to 5 m by 48 months without further cutting, with an expected increase in the width of the competition zone.

Gliricidia-maize Experiment, Rwanda (Ngoga et al.)

y = -0.0112x2 + 0.1803x + 0.2819

R² = 0.7034

0.4

0.6

0.8

1

2 4 6 8 10

Rela

tive Y

ield

Spacing (m)

0

2

4

6

8

10

d13/108 d13/36 d13/72 ST

Num

ber

of

sta

kes p

er

tree

Tree density and sole tree

trt

Average of skes1 Average of skes2

aabab

b

a

b

ab

a

0

1

2

3

4

5

6

ht36_m ht48_m

Tre

e h

eig

ht

(m)

Months after planting (36 and 48)

a

b

Overview of Modelling Aims and Progress• Add/complete active tree models:

– Gliricidia, Grevillea, Faidherbia, Alnus, Cordia and Eucalyptus

• Add/complete crop models

– potato, teff, bush beans, climbing beans (maize and wheat already available)

• Conduct regional projections and virtual experiments

• Link plot-scale (APSIM) outputs with farm- and landscape-scale models (SIMILE, Polyscape)

• Use APSIM in context-sensitive participatory design

• Use models to inform policy and practice dialogues - World Vision

• Conduct soil and plant measurement workshop

• Training and student supervision

CSIRO Land and WaterPhilip Smethurst

t +61 3 6237 5653

e Philip.Smethurst@csiro.auw www.csiro.au

Thank you

Acknowledgements

Rwandan Agricultural Board Tenge

ACIAR Athanase

CSIRO Damascene

ICRAF

CIMMYT