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Agricultural Outlook for Cotton 2012
Darren Hudson, Director, Cotton Economics Research Institute, Texas Tech University
Maria Mutuc, Post-Doctoral Associate, CERI
39th Annual Bankers Agricultural Credit Conference, November 18, 2011International Cultural Center, Texas Tech University
_________________________________________________________________________________
Year in Review
• “Unmitigated disaster…” Me, Bloomberg, September 14, 2011• Although not the largest year-on-year percentage decline in production, by far the
largest loss in terms of crop value
• Insurance helped, but financial impact was real and if we see consecutive years of drought, we will start seeing farm failures.
• Weaker demand kept a lid on prices this year, despite the shortfall in production
• Stocks still tight, but more availability than in 2010; increase in economic activity will still put substantial upward pressure on prices, but outlook there is not particularly strong
Economic Impact of Drought 2011
State Production Losses by Crop, $US B
Livestock
Lost hay production
Cotton
Corn
Wheat
Sorghum
2.06
0.75
1.8
0.327
0.243
0.063
Source: Texas AgriLife
Effects on upstream & downstream industries
State-wide Economic Impacts by Crop, $US B
Backward
Linkages,
US$2.9 B
Forward
Linkages,
US$0.58
B
Livestock
Lost hay production
Cotton
Corn
Wheat
Sorghum
2.64
1.46
3.48
0.53
0.48
0.12
Source: Texas AgriLife
U.S. Futures Contracts Quotes (as of Nov 1)
Dec 2011 Contract Dec 2012 Contract
Largely irrelevant
Current momentum is sideways at 94-98.
A 94-98 futures translates to a local cash price of 87-91 cents.
U.S., 1990-2020f
Production (‘000 bales) and Yield (lbs/acre)
'90'91'92'93'94'95'96'97'98'99'00'01'02'03'04'05'06'07'08'09'10'11'12'13'14'15'16'17'18'19'200
5000
10000
15000
20000
25000
0
0.5
1
1.5
2
2.5
Production Yield (R-axis)Source: Cotton Economics Research Institute
U.S., 1990-2020f
Ending Stocks (‘000 bales) and Farm Price (cents/lb)
'90
'92
'94
'96
'98
'00
'02
'04
'06
'08
'10
'12
'14
'16
'18
'20
0
2000
4000
6000
8000
10000
12000
0
10
20
30
40
50
60
70
80
90
100
Ending Stocks Price (R-axis)
Source: Cotton Economics Research Institute
World, 1990-2020f
Production (‘000 bales) and Yield (lbs/acre)
'90
'92
'94
'96
'98
'00
'02
'04
'06
'08
'10
'12
'14
'16
'18
'20
0
20000
40000
60000
80000
100000
120000
140000
160000
0
20
40
60
80
100
120
140
160
World Production Cotton A-index (R-axis)Source: Cotton Economics Research Institute
India, 1990-2020f
Production, Mill Use and Net Exports (‘000 bales)
'90'91'92'93'94'95'96'97'98'99'00'01'02'03'04'05'06'07'08'09'10'11'12'13'14'15'16'17'18'19'20
-5000
0
5000
10000
15000
20000
25000
30000
35000
40000
Production Domestic Mill Use Net Exports
Source: Cotton Economics Research Institute
China, 1990-2020f
Production, Mill Use and Net Exports (‘000 bales)
'90
'92
'94
'96
'98
'00
'02
'04
'06
'08
'10
'12
'14
'16
'18
'20
-30000
-20000
-10000
0
10000
20000
30000
40000
50000
60000
70000
Production Domestic Mill Use Net Exports
Source: Cotton Economics Research Institute
Brazil, 1990-2020f
Production, Mill Use and Net Exports (‘000 bales)
'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 '19 '20
-4000
-2000
0
2000
4000
6000
8000
10000
12000
Production Domestic Mill Use Net Exports
Source: Cotton Economics Research Institute
Pakistan, 1990-2020f
Production, Mill Use and Net Exports (‘000 bales)
'90 '91 '92 '93 '94 '95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '15 '16 '17 '18 '19 '20
-6000
-4000
-2000
0
2000
4000
6000
8000
10000
12000
14000
Production Domestic Mill Use Net Exports
Source: Cotton Economics Research Institute
Conclusions
• Expectations for higher U.S. and world production, partially on yield gains, but some recovery of planted area as well.
• Longer-term moderation of prices, but still well above historical levels.
• Although definitively tied to ethanol production, US cotton prices are likely more responsive to weather events (supply-side) and economic growth and textile production in Asia (demand-side); need to pay attention to weather events in China and India (and to some extent Brazil and Australia) to adjust your expectations for price.