Opportunities from multi-dimensional crop improvement and ... · Opportunities from...
Transcript of Opportunities from multi-dimensional crop improvement and ... · Opportunities from...
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Opportunities from multi-dimensional crop improvement and the supporting role of Near
Infrared Spectroscopy(NIRS) networks
Michael Blümmel
International Livestock Research Institute
11 November 2016
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TopicsWhy pay attention to crop residues: feed supply-demand
scenarios, context, fodder markets
Impact of differences in crop residue fodder quality on livestock productivity
Exploit existing cultivar variations and targeted genetic enhancement, trade-offs
NIRS hubs as logistical support infrastructure for phenotyping in multidimensional crop improvement
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Feed resource supply - demand scenarios inIndia
Feed resource Contribution to overall feed resources (%)
Greens from CRP, forests, grazing 8.0
Planted forages 15.1
Crop residues 70.6
Concentrates 6.3
Deficit: feed availability versus feed requirement (%)
Dry matter (i.e. crop residue quantity) -6
Digestible crude protein -61
Total digestible nutrients -50
(NIANP 2012; Blümmel at al. 2014)
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0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
4 0 0 0
4 5 0 0
Yield differences in milk production between the 10% most productive farmers and the remaining 90% in India when managing
comparable dairy genetics
(Derived from VDSA-India 2013 and Blümmel et al. 2016b)
A ll C o w s
L o c a l C o w s
C ro s s b re d C o w s
1 0 %
1 0 %
1 0 %
9 0 %9 0 %
9 0 %
G A P : 2 8 1 7 k g G A P : 8 6 9 k g G A P : 2 3 0 9 k g
Av
era
ge
Mil
k Y
ield
(k
g/c
ow
/ye
ar)
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Crop residues are becoming more important
Kahsay Berhe (2004) study in Yarer Mountain area
Cultivated land has doubled at the expense of pasture in 30 years
Switch in source of nutrition for livestock from grazing to CR
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4 0 5 0 6 0 7 0 8 0 9 0
0
1 0 0
D ry m a tte r in ta k e
D ig e s tib le e n e rg y in ta k e
P h y s ic a l P h y s io lo g ic a l
F o d d e r q u a lity : h e re D ig e s tib ility (% )
Inc
re
as
ing
In
tak
e
S e t p o in t
R e la t io n s h ip b e tw e e n fo d d e r q u a lity
a n d v o lu n ta ry fe e d in ta k e
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Sorghum stover trading in Hyderabad
7
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Nov Dec Jan Feb Mar Apr May Ju Jul Aug Sep Oc Nov0
2
4
6
8
10
12
14
Sorghum grain
Sorghum stover
3.4
6.5
Month of trading
Ind
ian
Ru
pee p
er
kg
Yearly mean
2004 to 2005
Nov Dec Jan Feb Mar Apr May Ju Jul Aug Sep Oc Nov0
2
4
6
8
10
12
14
Sorghum stover
Sorghum grain
6.2
10.2
Yearly mean
2008 to 2009
Month of trading
Comparisions of average cost of dry sorghum stover traded in Hyderabad and average of cost ofsorghum grain in Andhra Pradesh 2005 to 2005 and 2008 to 2009
Changes in grain: stover value in sorghum
traded in Hyderabad from
2004-5 to 2008-9
Sharma et al. 2010
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Relation between digestibility and price of sorghum stover
44 45 46 47 48 49 50 51 52 53 54 552.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
y = -4.9 + 0.17x; R2 = 0.75; P = 0.03
Stover in vitro digestibility (%)
Sto
ver
pri
ce (
IR/k
g D
M)
Premium Stover
“Raichur”
Low Cost Stover
“Local Yellow”
Blümmel and Parthasarathy, 2006
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Price variations in different sorghum stover traded concomitantly in Mieso in April 2007
StoverETB/kg
Trader
ETB/kg
Farm
Sweet Sorghum (SS) 0.65 0.20
“Grain” Sorghum (GS) 0.50 0.13
Price premium 30% 54%
Source: calculated from Gebremedhin et al. 2009
Note: In India SS stover have about 3-4 units higher digestibility than GS stover
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Price: quality relations in rice straw traded monthly in Kolkata from 2008 to
2009
37.0 37.5 38.0 38.5 39.0 39.5 40.0 40.5 41.0 41.5 42.02.75
3.00
3.25
3.50
3.75
4.00
4.25
Best (n=81)
Good (n=260)
Medium/low (n=273)
In vitro digestibility of rice straw (%)
Pri
ce o
f ri
ce s
traw
at
Ko
lkata
tra
ders
fro
m 2
008-2
009 (
Ind
ian
Ru
pees/k
g)
Teufel et al. (2012)
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Conclusions: why pay attention to crop residues as fodder
Feed supply – demand scenarios underline key role of crop residues as feed sources
Fodder market surveys show high monetary value, narrowing crop residue-grain price ratios
Driving factor: more quality fodder required with shrinking natural resource basis
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Impact of variations in crop residue fodder quality on livestock productivity
Effect of superior stover as basal diet
What magnitude of fodder quality differencematter and why
Livestock productivity levels on entirely crop residue based diets
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Feed block manufacturing: supplementation,
densification
Ingredients %
Sorghum stover 50
Bran/husks/hulls 18
Oilcakes 18
Molasses 8
Grains 4
Minerals, vitamins, urea 2
Courtesy: Miracle Fodder and Feeds PVT LTD
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Relation between digestibility and price of sorghum stover
44 45 46 47 48 49 50 51 52 53 54 552.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
y = -4.9 + 0.17x; R2 = 0.75; P = 0.03
Stover in vitro digestibility (%)
Sto
ver
pri
ce (
IR/k
g D
M)
Premium Stover
“Raichur”
Low Cost Stover
“Local Yellow”
Blümmel and Parthasarathy, 2006
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Comparisons of feed blocks based on lower (47%) and higher (52%) digestible sorghum stover and tested
with commercial dairy buffalo farmer in India
Block Premium Block Low
CP 17.2 % 17.1%
ME (MJ/kg) 8.46 MJ/kg 7.37 MJ/kg
DMI 19.7 kg/d 18.0 kg/d
DMI per kg LW 3.8 % 3.6 %
Milk Potential* 15.5 kg/d 9.9 kg/d
Modified from Anandan et al. (2009a)
* 21 and 14 kg/d in crossbred cattle
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Live weight gains in Indian Deccan sheep fed exclusively on groundnut haulms
Groundnut cultivars Gain (g/d)
ICGV 89104 137
ICGV 9114 123
TMV 2 111
ICGS 76 76
ICGS 11 76
DRG 12 66
ICGS 44 65
ICGV 86325 83
ICGV 92020 95
ICGV 92093 109
Prob > F 0.02
Prasad et al. 2010
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Live weight changes in Ethiopian Arsi-Bale sheep fed exclusively on Faba bean straws
Wegi et al., 2016
Cultivars Grain Yield Straw Yield Weight Gain (g/d)
Mosisa 4.28a 5.68a 52.2ab
Walki 4.21a 4.42c 64.6a
Degaga 4.20a 4.31c 43.2bc
Shallo 4.06a 4.98b 37.5c
Local 2.89b 3.65d 48.3bc
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Conclusions: Impacts of variations in crop residue fodder quality on livestock productivity
“Intuitively” small difference in fodder quality of stover do matter: additive effect of higher
diet quality and higher intake
Informed choice of cultivar can have very substantial effect on livestock
productivity
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Exploit existing cultivar variations
Phenotyping new cultivars submitted for release testing for fodder traits
• Laboratory infrastructure: stationary NIRS, mobile NIRS
Phenotyping during crop improvement for fodder traits
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Stover fodder trait analysis in new sorghum cultivar release testing in India 2002 to 2008
(Blümmel et al. 2010)
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Cultivar CP IVOMD ME SY
% % MJ/kg kg/ha
ICSC 93046 7.6 58.8 8.87 15 814
ICSV 91005 7.5 58.3 8.75 17 734
ICSR 196 7.9 55.2 8.19 10 386
ICSR 56 6.9 54.8 8.23 9 698
NT J2 6.6 54.8 8.19 11 675
E-36-1 7.0 53.7 8.00 9 256
ICSR 93034 6.6 53.6 8.00 10 176
A 2267-2 6.3 52.6 7.82 10 046
Seredo 6.1 52.6 7.86 8 069
ICSV 96143 6.6 51.8 7.64 6 295
WSV-387 6.5 51.7 7.64 7 911
ICSV 111 5.9 50.8 7.56 7 372
Statistical summary
LSD 0.5 1.6 0.25 1 726
h2 0.65 0.49 0.50 0.47
South-south transfer of superior dual purpose sorghum cultivars: tested 2 years x 3 locations in Ethiopia
Adie et al. 2016
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Conclusions: exploit existing cultivar
variations
Livestock nutritionally-significant variations exist in all key cereal and legume crops (except perhaps wheat)
Short impact pathways, quick, relatively little investment
Modifying cultivar release criteria promising entry point
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Targeted genetic enhancement
Targeted genetic enhancement towards dual and multi purpose traits
Conventional breeding (recurrent selection, hybridization)
Molecular breeding (QTL introgression, Genetic
selection)
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Response in stover in vitro digestibility to 2cycles of selection of pearl millet
variety ICMV 221
Digestibility%
Grain Yieldkg/ha
Stover yieldkg/ha
Original 43.6 2 669 3 095
H1 44.5 2 596 3 460
L1 42.1 2 592 2 889
H2 45.8 2 564 3 168
L2 42.0 2 408 2 731
25
Choudhary et al (in preparation )
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Effect of introgression of different stay green QTL’son stover digestibity of a Rabi
sorghum background
(Blümmel at al. 2015)
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(Blümmel at al. 2015)
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Dual Purpose Maize: Genomic Selection
(Babu et al. 2016)
DTMA & CAAM New DH Lines
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ID IVOMD - Predicted IVOMD-ObservedDH_9_157 High IVOMD and ME 57.1DH_3_33 High IVOMD and ME 56.7DH_3_63 High IVOMD and ME 55.8DH_9_15 High IVOMD and ME 55.7DH_8_4 High IVOMD and ME 55.6DH_3_149 High IVOMD and ME 55.5DH_3_24 High IVOMD and ME 55.4DH_6_1 Low IVOMD and ME 55.4DH_3_10 High IVOMD and ME 55.0DH_3_21 High IVOMD and ME 54.9DH_3_138 High IVOMD and ME 54.6DH_3_35 High IVOMD and ME 54.5DH_3_61 High ME 54.4DH_3_83 High IVOMD and ME 54.1DH_9_165 High IVOMD 53.6DH_9_134 High IVOMD 53.6DH_9_153 High IVOMD and ME 53.5DH_3_47 High IVOMD and ME 53.4DH_3_62 High IVOMD and ME 53.4DH_3_87 High IVOMD and ME 53.4DH_3_82 High IVOMD 53.3
Predicting performance of DH maize lines for fodder quality
HTMA - GSPred.
Accuracy
IVOMD 0.44
ME 0.45
(Babu et al.2016)
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4 5 .0 4 7 .5 5 0 .0 5 2 .5 5 5 .0 5 7 .5 6 0 .0
2 .8
3 .0
3 .2
3 .4
3 .6
3 .8
4 .0
4 .2
S to v e r in v itro d igestib ility (% )
Sto
ve
r p
ric
e (
IR/k
g D
M)
B re e d in g a d v a n c e in d u a l p u rp o s e m a iz e s to v e r fo d d e r q u a lity
re la t iv e to d iffe re n t s o rg h u m s to v e r tra d e d in ra in fe d
In d ia in th e p a s t d e c a d e
L o w q u a lity
s o rg h u m s to v e r
H ig h q u a lity
s o rg h u m s to v e r
M e a n IV O M D (ra n g e 5 5 .2 to 5 7 .9 % )
o f 1 1 a d v a n c e d d u a l p u rp o s e m a iz e
b re e d in g lin e s g e n e ra te d
d u r in g th e p ro je c ts
M e a n IV O M D (ra n g e 5 3 .6 to 5 6 .0 % )
o f 1 1 e x p e rim e n ta l h e a t to le ra n t d u a l
p u rp o s e m a iz e h y b r id s g e n e ra te d
d u r in g th e p ro je c ts
B lü m m e l e t a l. (2 0 1 6 a )
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Conclusions: targeted genetic
enhancement
Longer term, higher investments
Great impact opportunities
Multi-trait options
New tools becoming available and more affordable
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Required infrastructure for
phenotyping for crop residue fodder quality
Stationary Near Infrared Spectroscopy (NIRS)
Mobile NIRS
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Qualitative trait prediction in plant breeding based
on Near Infrared Spectroscopy (NIRS)
Non-evasive
c. 200 samples/d
>30 traits
Physico-chemical
c. 60 000 US $
Calibration
Validation
NIRS equations sharable across
compatible instruments
At current: ILRI
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34
Transfer of NIRS equations for phenotyping for fodder
quality traits: example cowpea
ILRI FOSS 5000
ILRI FOSS 6500
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Methionine Prediction by NIRS
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Mobile handheld NIRS
• About 40 000 US$ but pricedecreasing
• Application currently developed and validated at ILRI India and Ethiopia
Luminar 5030
Phazir
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Conclusions: NIRS infrastructure for phenotyping in multidimensional crop improvement
Multi-dimensional crop improvement can be mainstreamed using NIRS
NIRS hubs minimize new investments and optimzeolder ones (South Asia, East Africa, West Africa)
Sample grinding the real bottleneck and rate limiting procedure
Mobile NIRS a way out?
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Where to go from here
Develop the East African NIRS hub based on ILRI-EIAR-Private Sector collaboration
Screen released and pipeline key cereal and legumes crops for food-feed-fodder traits
Explore feed and fodder value chains around improved crop residues
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CGIAR Research Program on Livestock and Fish
livestockfish.cgiar.org
The CGIAR Research Program on Livestock and Fish aims to increase the productivity of small-scale livestock and fish systems in sustainable ways, making meat, milk and fish more available and affordable across the developing world.
This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
The Program thanks all donors and organizations who globally supported its work through their contributions to the CGIAR system.