residue soil moisture and yield interactions under ca: initial evidence from simlesa
TRANSCRIPT
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Understanding residue management, soil moisture and maize yield interactions under CA: Initial
evidences from SIMLESA
Isaiah NyagumboGlobal Conservation Agriculture Program
+Team SIMLESA Malawi & Mozambique
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1. Introduction
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CA and residue cover provision• Provision of permanent residue cover is one of the three principles
upon which Conservation Agriculture is hinged.
• Competition between using residues as cattle feed or leaving it for soil cover provision remains a lively discussion point among CA researchers (Valbuena et al 2012); Baudron, et al., 2014)
• It is recommended farmers provide permanent residue cover and use at least 30% residue cover or 2-3t/ha by time of planting.
• In Malawi the low numbers of livestock make the use of residues for soil cover a relatively easy undertaking.
• In Mozambique despite the low livestock numbers, termites pose a serious threat to residues left in fields especially during the dry winter months.
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Crop-livestock competition and implication for CA-based intensification
(Data from Adoption Pathways)
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0
1000
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5000
6000
Av CMP-cum.soil loss (kg/ha)
MR-avg-cum.soil loss (kg/ha)
Date
Dai
ly r
ainf
all (
mm
)
Soil
loss
(kg/
ha)
Total rainfall = 774 mmTotal Erosivity = 9647 J/m2
Total rainfall = 481 mmTotal Erosivity = 9694 J/m2
Total rainfall = 957 mmTotal Erosivity = 13 919 J/m2
Cumulative soil loss (kg/ha) at Hatcliffe, Har-are
Is Soil Cover important with regards to land degradation?
soil loss target= 3.5t/ha/yr
Source: Nyagumbo (2011), WCCA, Brisbane paper
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Cum
mul
ative
run
-off
or D
aily R
ainfa
ll (m
m)
Rainfall (mm)
Av CMP-cum.runoff(mm)
Av-MR-cum.runoff(mm)
Error bars denote +/- SE of mean
Effects of CA and Conventional Mouldboard Ploughing on cumulative runoff in 3 consecutive seasons at Hatcliffe
Total seasonal run-off amounted to 7.4,16 and 8.1 % of seasonal rainfall for CMP compared to 0.5, 0.8 and 0.6 % for CA over the 3 seasons
Total rainfall 774 mm Total rainfall 481 mm Total rainfall 956 mm
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How SIMLESA seeks to contribute to SI
Develop productive, resilient & sustainable CA based maize-legume intensification systems across the major agro-ecologies in 5 ESA countries
CA as a set of technologies to • To increase maize and legume
productivity by 30%– through improved maize, legume varieties,
forages and associated management practices, – with adoption enabled and motivated through the
development of markets and value chains• To reduce downside yield risks by 30% • To benefit at about 650,000 farm
households (until 2020)
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Sustainable Intensification
Sustainability•Conserve the natural resource base (Godfray et al., 2010; Pretty et al., 2011; Tilman et al., 2011)
•Ecologically and technically sound eg soil quality degradation through erosion, fertility decline
•Socially and Culturally acceptable ( Do the technologies fit local farming systems?)
•Economically viable (does it make economic sense?)
Intensification•Increased yield or outputs per unit area/inputs (Enhance productivity)•Diversification from maize for diet diversification and improved incomes•Integration of crops & livestock•Improved resilience to market shocks and climate risks •Improved efficiency per unit input eg water, labour, capital, inputs
Improved food security and livelihoods
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30% yield risk reduction+ 30% productivity increase among 650000 farms by 2023
Obj 1: Enhanced the understanding of CA-based intensification options for maize-legume production systems, value
chains and impact pathways
Obj 2: Adaptation of productive, CA-based intensification options for sustainable smallholder maize-legume production systems
Obj 3: Increased range of maize, legume and fodder/forage varieties available for smallholders
Typologies & farmscale
studies
Objective 4: Outscaling & Innovation Systems
•Variety preferences•Value chains•Markets
CA meta-analysis
Varieties for CA
Smart sequencing seed road map; business models
Integration
intensification
impact
SIMLESA-2: 2014-18
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Which countries did we target?
Major maize growing agro-ecologies across 5 core countries:•Ethiopia•Kenya•Tanzania •Malawi•Mozambique
Plus lean activities in 3 spillover countries
•Botswana•Rwanda•Uganda
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SIMLESA strategy
Community awareness meetings
Farmer consultations and agreement on treatmentsIdentification of 6 host farmers per community
On-station trials:
Exploratory on-farm trials establishment and monitoring:
Outscaling activities through IPs and partnerships
Farmer field days
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2. Study Objectives
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This study sought to
• Understand and compare residue management practices on SIMLESA on-farm sites in Malawi and Mozambique
• Evaluate residue and nitrogen application rates effects on maize yields.
• Explore linkages between residue management, soil moisture and maize yield in CA
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3. Methods
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How was this done?1. Physical assessments of residue application rates
on on-farm trial sites in different agro-ecologies of Malawi and Mozambique and % cover estimation using photo comparison method (Shelton and Jasa, 1995) at the on-set of the cropping season.
2. Analysis of seasonal maize yields from residue*nitrogen on-station trial at Chitala, Malawi from 3 seasons: 2011/2, 2012/13 and 2013/14.
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4. Some key findings
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Residue management practices
What is the amount of residue leftin the field?
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Established relationship between maize residue rates and estimated % cover using data from, Malawi and
Mozambique
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Residue cover application is a challenge in termite prone and crop-livestock environments (eg Mw vs Mz)
Residue application well above 3t/ha in environments with no livestock, but well below recommendations in livestock and termite infested environments!
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Mitundu mchinji kasungu Salima Balaka Ntcheu Manica SussundengaMalawi-Mid-altitude Malawi-lowlands Mozambique-Central
0.00
5.00
10.00
15.00
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25.00
30.00
a
a
b
aa
b
a
b
Mea
sure
d re
sidu
e ra
te D
ec 2
014
(t/h
a)Residue cover application is a challenge in termite prone and
crop-livestock environments (eg Mw vs Mz)Dec 2014
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Residue /yield relationships by agro-ecology in Malawi
• R
•Could simply be higher biomass higher yield scenario or Indeed•Increased residues result in higher yields ( weed suppression, moisture conservation etc)
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Residue-yield relationships in different mid-alt districts, Malawi
•Well drained soil•Medium to heavy texture•Low incidence of pests and leaf diseases=> Residue increase pays
•Well drained soil•Medium to heavy texture•high incidence of pests and leaf diseases=> Yield loss penalties for using residues
•Poor drainage•Coarse texture •high incidence of pests and leaf diseases=> Yield loss penalties for using residues
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Do on-station trial results suggest otherwise.....?
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18-Nov 18-Dec 17-Jan 16-Feb 18-Mar 17-Apr0
100
200
300
400
500
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700
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1000
643.4 mm
771.4 mm
2011/12 2012/13 2013/14
Time
Daily
cum
ulati
ve ra
infa
ll (m
m)
Cummulative rainfall at Chitala, Malawi over 3 seasons 2011/12 to 2013/14
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APSIM model suggests residues contribute to some significant N-lock up (>10%)
N-lock-up
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Maize grain yields over 3 seasons under different residue and N rates at Chitala, Malawi
N.B. Significant yield increases from CA only observed in the third season!
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Residue and N rates effects on maize yields over two seasons at Chitala,
Malawi
•N applications significant right from the beginning!•Some N-lock up apparent in no N situations when residue rates increase from 0 to 2t/ha reduces with time!
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Optimum yields seem to feature at 4-5 t/ha irrespective of N fert rate!
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.00
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f(x) = − 120.369318181818 x² + 1119.48863636364 x + 5950.04545454545R² = 0.952014888733051
f(x) = − 42.3579545454545 x² + 397.365909090909 x + 5879.73636363636R² = 0.669510045837806f(x) = − 95.4034090909091 x² + 906.356818181818 x + 4506.97272727273R² = 0.953351002088398
f(x) = − 39.3125 x² + 461.375 x + 2619.5R² = 0.80742719510821
0 Polynomial (0) 30 Polynomial (30)
Residue application rate (t/ha)
Mai
ze g
rain
yie
ld (k
g/ha
)
Optimum residue rate?
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How is CA and residues impacting on soil
moisture.....?
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Measured soil moisture effects in lowlands and Mid-altitude areas of
Malawi in Feb 2013
N.B CA techniques consistently portray higher moisture status!
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Residues effectively enhance water infiltration and minimize run-off!
N.B Pictures taken from Ntcheu 2012!
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...and similar effects in Mozambique(Angonia, March 2012)
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5. Lessons learnt
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Key Emerging lessons• There are major differences in farmers capacity to apply
recommended residues rates in different agro-ecologies, attributed mostly to livestock densities and termite activity in Southern Africa and dependent on local agro-ecological conditions hence
– Farmers in Mozambique have residue cover levels below the recommended 3t/ha while those in Malawi are way above this threshold after 3 seasons of CA!
– Low rainfall areas also suffer more difficulties in meeting recommended residue application rates eg Balaka
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.........Key Emerging lessonsApparently farmers site residue application/ importation
as one of the most labour demanding tasks in CA
• Results suggest increased residue application rates may be beneficial to yields on well drained soils but counterproductive in waterlogged conditions , in the presence of maize leaf diseases and pests.
• Optimum application rates however could lie between 4-5 t/ha ( still to be verified further).
• Advantage of CA in soil moisture improvement is apparent.
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Acknowledgements• NARS (Mozambique, Malawi)• ACIAR • CIMMYT • QAAFI
• Farmers
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Thank you!!