understanding existing spatial variability diagnostics at regional scale

International Institute of Tropical Agriculture – Institut international d’agriculture tropicale – www.iita.org

Understanding existing spatial variability

diagnostics at regional scale




12012014712884Farms (n)

44559Sites (n)

Cycle = the period between subsequent harvests from a single mat. This value is around 1 year at 1200 m.a.s.l. but increases with altitude

Sites = nr. of districts (Uganda) or villages (Rwanda, Burundi, North Kivu, South Kivu)

The top and bottom of the error bars represent the maximum and minimum site average yield per region/country

12012014712884Farms (n)

44559Sites (n)

Cycle = the period between subsequent harvests from a single mat. This value is around 1 year at 1200 m.a.s.l. but increases with altitude

Sites = nr. of districts (Uganda) or villages (Rwanda, Burundi, North Kivu, South Kivu)

The top and bottom of the error bars represent the maximum and minimum site average yield per region/country

0

10

20

30

40

50

60

70

Uganda Rwanda Burundi North Kivu South Kivu

Yie

ld t

ha

-1cycle

-1

0

10

20

30

40

50

60

70

Uganda Rwanda Burundi North Kivu South Kivu

Yie

ld t

ha

-1cycle

-1



from Lake Victoria basin to Albertine rift

Central Uganda

15 t/ha/cycle

East Rwanda

East Burundi

SW Uganda

25 t/ha/cycle

1100m 1300-1400m

1600-

2100m

Rusizi

SemlikiKivu

Region

45 t/ha/cycle

Rainfall

Pest and disease pressure

Soil fertility

Plant densities


Ruhango

Nzenga

Munoli

Lurhala

Luhihi

Kirundo-

Kibuye

Kibungo

Kaliva

Kabam

ba

Gitega-2

Gitega-1

Cyanangu

Cibotoke

Burhale

Bingo

K (

% o

f dry

matt

er)

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5



Drought major constraint <1200 mm/yr

Soil fertility highly variable, but generally

better near Albertine rift

High foliar K conc = high productive sites

Pest pressure low > 1300m

BBTV and BXW ‘restricted’ to hotspots

Conclusion: abiotic stresses very important


Yield gap analysis in Uganda



diagnostics at village scale

‘Poor’ versus ‘Rich’ farmers:

• Yields

• Arable land

• Hired labor

• Livestock

• External revenues

• Commercialization

Conclusion:

Large differences in access

to resources → technology

choice

PhD thesis ongoing on farmer

innovation in GL region

Poor Medium Rich

Number of farms 17 28 5

Banana performance

Bunch weight 16.2b 16.4b 20.1a

Average spacing (m) 2.2 2.3 2.2

Land, livestock, and labor

Tot. arable land (ha) 0.32b* 0.48b 0.55a

% land und. banana 70 70 70

Hired labor (man/day) 0.5b 0.8b 3.2a

Cows (nr) 0.77 2.2 3.0

Soil Org Carbon (%) 1.3 1.3 1.4

Weevil damage (XT %) 4.0 5.4 4.4

Income sources

Earning salary % 0b 4b 40a

Ext. financial sup. % 24b 50ab 80a

% farms selling ban. 47b 75a 100a

*Letters behind numbers in the same row indicate significant differences (p<0.1)



diagnostics at farm scale

Delstanche, van Asten, Gaidashova, Delvaux – Eurosoil conference

MSc thesis I.A. Newton

Most still needs to be published


Understanding existing temporal variability

on-farm monitoring study in SW Uganda

150 plants - 10 farms - 2 years

• Peak production May – Oct→ prices are low

• Low production Nov – Feb→ prices are high

Sucker emergence → Harvest date

• Give preference to suckers emerged in Q1 over those that emerged in Q4

• Farmers prefer desuckering in Dec-Jan, but they should then leave the smallest, not the biggest suckers

Got Matooke for Christmas →

Birabwa, van Asten, Newton, Taulya,

Mombasa presentation – to submit to Act Hort


Understanding existing cropping systems

Comparing banana-coffee mono and intercrop

APEP-funded project

300 farmer fields in Uganda

• Bananas do not reduce (<13%)

coffee yields, but Robusta

banana yields

• Banana intercrop generates

+ 700 $/ha/yr in Robusta

+ 1900 $/ha/yr in Arabica

van Asten, Mukasa, Uringi

poster Mombasa – Act Hort, to be submitted

R4D review feature story

PhD research on banana-coffee

systems has started in Burundi

under CIALCA-II


Pushing system components to their boundaries

Drought trials

Pot trials

a. Four cultivars (AAA-EA, AAA, ABB, AB)

b. Three moisture treatments

no stress: pF 1.8 – 2.1

moderate stress: pF 2.5 – 2.7

strong stress: pF 2.8 – 2.9

c. Measure stress (e.g. stomatal conductance)

c. Determine water use efficiency

d. Use findings to validate field results

Interim results: bananas don't look stressed

when they actually are!

Planning field trial in CIALCA-II project

Results from the above not published thus far

Field trial results - Nyombi et al (PhD reseach) in 2009

Rain

fall

(mm

)

0

15

30

45

60

75

90

Dec 2004 June 2005 Dec 2005 June 2006 Dec 2006 June 2007 Dec 2007

2005 - 1206mm

2006 - 1380mm

2007 - 935mm

B

0

15

30

45

60

75

902005 - 1034mm

2006 - 1334mm

2007 - 1633mm

Sept 2004 Mar 2005 Sept 2005 Mar 2006

A

A

Sept 2006 Mar 2007 Sept 2007

Cycle 2

Cycle 1

Cycle 3

Cycle 1

Cycle 2

0.05

0.15

0.25

0.35

0.45

Saturation (pF 0) Field capacity (pF 2) Wilting point (pF 4.2) 0-30 cm 30-60 cm 60-90 cmC

April 2005 Nov 2005 June 2006 Dec 2006 July 2007

0.05

0.15

0.25

0.35

0.45

Saturation (pF 0) Field capacity (pF 2) Wilting point (pF 4.2) 0-30 cm 30-60 cm 60-90 cm

June 2005 Jan 2006 Aug 2006 Feb 2007 Sept 2007

D

Volu

metr

ic m

ois

ture

conte

nt

(m3 m

-3)


Testing the hypotheses in the field

Nutrient omission trials

1. Setupa. Central and Southwest Uganda

b. N, P, K, Mg, Zn, S, B, Mo

c. Target yield 50 t/ha/yr

2. Preliminary findings after 2-3 cycles

a. K is most deficient

b. Fertilized yields poor (< 30 t/ha/yr)

c. Drought stress is a major problem

d. Ferralsols soils → poor root systems

e. Fertilizer improves sensory quality

0

2000

4000

6000

8000

0 200 400 600 800

K uptake (kg ha-1

)

0N-0P-0K 0N-50P-600K150N-50P-600K 400N-0P-600K

400N-50P-0K 400N-50P-250K400N-50P-600K Max dillutionMax concentration

Banana f

inger

bio

mass (

kg h

a-1

)

FULL

- K

Horizontal distance from center of pseudostem (cm)

Ro

otin

g d

ep

th (

cm

)

-150 -100 -50 0 50 100 150

-100

-50

0

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

Nyombi, van Asten, et al.: Draft ready → submit Feb 2009

Taulya, van Asten, et al: Global plant sci book - submitted



Optimal mulch thickness

On-station trial at ISAR, Rwanda

1. 0 cm mulch

2. 5 cm mulch

3. 10 cm mulch

4. 20 cm mulch

With and without shading

Soil moisture monitoring

→ 5 cm already very effective

Soil chemical properties

→ improvement proportional to application

Van Asten, Twagirayezu, Gaidashova

Rwanda Agricultural Conference

Presentation and paper, 2007. Week Number (1 = 9 Sept)

12111098765321

Soil

Mois

ture

Conte

n (

Vol %

)

30

20

10

0

1.00

2.00

3.00

4.0020 cm

10 cm

5 cm

0 cm

Mulch rates



Mulch and zero-tillage trials

Setup in CIALCA project

8 researcher-managed trials

in Rwanda, Burundi, and DRC

1. Mulch removal + tillage

2. Self-mulch + no-till

3. Trypsacum + no-till

4. Hyparrhenia + no-till

All intercropped with bush beans

Objectives

• Impact on nutrient stocks and flowsPhD thesis Syldie Bizimana (ISABU - Burundi)

• Impact on soil physics and root systemsPhD thesis Tony Muliele (INERA - DR Congo)

• Impact on banana + bean crop performanceMSc thesis Agnes Mukdandida (ISAR- Rwanda)



other field and lab trials

Planting density trials in Rwanda

• from 1000 – 5000 plants/ha

• 3 different cultivars

• 3 contrasting agro-ecologies

• compare with farmer practicesPhD thesis Telesphore Ndabamenye (ISAR-Rwanda)

CIALCA-Bioversity sponsored

Abuscular Mychorrizal Fungi (AMF)

• On-farm diagnostics and pot trials Rwanda

• Diagnostics and field trial in Kenya, Uganda

Mulch x Nematode trial in Rwanda

• Establish yield loss due to P. GoodeyiPhD thesis Svetlana Gaidashova (ISAR-Rwanda)

Collaboration in Kenya with TSBF

AMF research presented in Mombasa conference

Paper on AMF on-farm diagnostics to be submitted


Crop growth and nutrient response models

Ugandan potential yield – 112 t/ha/yr

Crop growth model based on:

1. Light interception and L.U.E.

2. Temperature sum and biomass partitioning

3. Water limited yield

4. Nutrient (N, P, K) limited yield (QUEFTS)

PhD research of Kenneth Nyombi (Wageningen University)

PARINT

PTRAN

DAvtmp

TRANRF

W lv, g W lv,d

Tbase

dW/dt

Tsum

dTsum/dt(dW/dt) lv,d

(dW/dt) lv

(dW/dt) st

(dW/dt) rt

(dW/dt) su

(dW/dt) bu

W st

W rt

W su

W bu

dGLAI/dt

dDLAI/dt

LAIDTR

TRAN

W co

(dW/dt) co

LUE

KDF

WATER

EXPLOR

EVAPO

W rt,d(dW/dt) rt,d

RAIN

RNINTC

DRAIN

RAIN

RNINTC

RAIN

RNINTC

RAIN

RNINTC

RAIN

RNINTC

RN

PARINT

DTR

SLA

A

CB

+

=


On-farm testing of best-bet technologies

the APEP project

APEP demo plots

• application of blanket NPK fertilizer

• 94 demos versus 84 control

• Demos yield 25 – 100%

• MRR > 500% close to Kampala

• MRR < 100% beyond Masaka

Conclusion:

Fertilizer only profitable near Kampala

New fertilizer recommendations

Banana and Coffee taking into account

• nutrient deficiencies (= region)

• target yield (= resource availability)

Van Asten et al., APEP final technical report

Wairegi, van Asten, et al. AFNET conference paper

Van Asten et al., Mombasa presentation, Act Hort

Uganda matooke farm gate bunch price

0

100

200

300

0 50 100 150 200 250 300 350

Distance to Kampala

Bu

nch

p

rice (

US

H/k

g)


Understanding existing cropping systems

Comparing banana-coffee mono and intercrop

APEP-funded project

300 farmer fields in Uganda

• Bananas do not reduce (<13%)

coffee yields, but Robusta

banana yields

• Banana intercrop generates

+ 700 $/ha/yr in Robusta

+ 1900 $/ha/yr in Arabica

van Asten, Mukasa, Uringi

poster Mombasa – Act Hort, to be submitted

R4D review feature story

PhD research on banana-coffee

systems has started in Burundi

under CIALCA-II


Projected banana-coffee work

Output 4 Capacity &

synergies

Capacity development

Partnerships & training materials

Scientific synergies

Output 3

Socio-economics Strengthen coffee-value chain through:

Determinants for investments

Access to input markets

Output markets (niches)

Organisational structures

Output 1

Crop physiology Effect of banana shade under different levels of water and nutrient stress on:

Coffee yield (quantity, quality)

Photosynthetic capacity

Pest & disease pressure

Trade-off analysis

Plant arrangement recommendations

Output 2

Agronomy Identify and test improved soil and water management technologies

Identify drivers of productivity

Map nutrient deficiencies

Participatory testing

Cost-benefit analysis

Improved soil and water practices

Empowerment of coffee actors

organ plant field farm farm organisation market

IITA NARO

AIT IFPRI

Figure 2: Relational diagram showing the relationship between the research conducted for the four outputs, the spatial level that the research activities primarily target, and the technical backstopping domains of the project research partners.

Strengthening coffee-value chain

understanding existing spatial variability diagnostics at regional scale

Business