Eugenio Arima The University of Texas at Austin, Department of Geography and the Environment [email protected] Land Use Spillover and Leakage Effects: Towards Integrating Concepts, Empirical Methods, and Models Berlin, Nov 9-10 2017 Agricultural Intensification and the Spatial Economy: Implications for Extensive Frontiers in Brazil
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Eugenio ArimaThe University of Texas at Austin, Department of Geography and the
Land Use Spillover and Leakage Effects: Towards Integrating Concepts, Empirical Methods, and Models
Berlin, Nov 9-10 2017
Agricultural Intensification and the Spatial Economy: Implications for
Extensive Frontiers in Brazil
Definition
• Intensive systems: mechanized agriculture (e.g.
soy, corn, cotton, sugarcane)
• Extensive system: cattle ranching on planted
pastures
Soybean-Sugarcane/Cattle Spatial Dynamics
Soybeans
Source: IBGE
Soybean-Sugarcane/Cattle Spatial Dynamics
Sugarcane
Source: IBGE
Soybean-Sugarcane/Cattle Spatial Dynamics
Cattle ranching
Source: IBGE
Goals1. Could intensive systems (e.g. soybean, corn, cotton & sugarcane) be pushing cattle ranching to Amazonia and cerrado?• Simple story (conjecture):
• Intensive systems encroachment on pastures drives pasture land prices up Relocation to cheaper regions
2. Land price data should be used more often in land science analyses
Conceptual Setup • Two land systems, Intensive and Extensive• Intensive: mechanized agriculture• Extensive: cattle ranching on planted pastures
Relocation Mechanism 1) Price Effect
Arima, E. Y., Richards, P. & Walker, R. T (in press). Biofuels production expansion and the spatial economy: implications for the Amazon basin in the 21st century. In: Bioenergy and Land Use Change, eds Qin Z, Mishra U., & Hastings A. John Wiley & Sons, Inc., Hoboken, NY.
Relocation Mechanisms: 2) Capital Constraint Relaxation Effect
Arima, E. Y., Richards, P. & Walker, R. T (in press). Biofuels production expansion and the spatial economy: implications for the Amazon basin in the 21st century. In: Bioenergy and Land Use Change, eds Qin Z, Mishra U., & Hastings A. John Wiley & Sons, Inc., Hoboken, NY.
Land Prices
Pasture land prices in the absence of competing uses
Pasture Land Prices where Intensive Systems are Viable
Pasture land prices
Pasture land prices when intensive cropping systems are viable
Pasture land prices will be higher because they reflect intensive system use, not cattle ranching revenue
Likely Mechanism at play if:1) Pasture is being replaced by intensive systems
2) Pasture land prices in intensive areas are
overpriced
• Revenue (price and productivity) explain only part of
pasture price differences
3) Favorable “terms of trade”:• Pasture lands in intensive regions >> forested lands in extensive
regions
Datasets
• Land prices• FNP Agrianual Almanacs 2001 to 2016• 93 out of 133 regions (excluded most of NE, RJ, ES)• 12,104 records• Forest & Cerrado, Cropland, Pasture, others• 1-4 price point information per region per year per LULC• Used median price in this analysis
FNP Regions
Larger than Municipalities
Smaller than States
Datasets• Land Cover and Use
• MapBiomas 2000 to 2016, Landsat based, 30 m resolution, resampled to 600 m.
• Forest, Cerrado, Cropland, Pasture, others• Calculated land cover statistics for each FNP region
MapbiomasData
http://mapbiomas.org/pages/downloads
Intensive: croplands, sugarcane, mixed croplands
Extensive: planted pastures, planted pastures on grasslands
Likely Mechanism at play if:1) Pasture is being replaced by intensive cropping systems2) Pasture land prices in intensive areas are overpriced
• Revenue (price and productivity) explain only part of pasture price differences
3) Favorable terms of trade:• Pasture lands in intensive regions >> forested lands in extensive
regions
Pasture area trends display two distinct dynamics
Cropland trends are positivein most places
Regional trendsCroplands & Pastures
Likely Mechanism at play if:1) Pasture is being replaced by intensive cropping systems2) Pasture land prices in intensive areas are overpriced
• Revenue (price and productivity) explain only part of pasture price differences
3) Favorable terms of trade:• Pasture lands in intensive regions >> forested lands in extensive
regions
Ratio of pasture land prices
Do cattle price and productivity differentials explain pasture land price discrepancy?
• Higher pasture land prices in intensive cropping regions could be due to higher revenue• Higher productivity• Higher farmgate prices
Price & Productivity explains only part of land price differential
Likely Mechanism at play if:1) Pasture is being replaced by intensive cropping systems2) Pasture land prices in intensive areas are overpriced
• Revenue (price and productivity) explain only part of pasture price differences
3) Favorable “terms of trade”:• Pasture lands in intensive regions >> forested lands in extensive
regions
Terms of trade pasture vs. forests in Amazonia
Terms of Trade averages: pasture lands in expanding cropping areas vs. forests in Amazonia
Net Land Allocation 2000-2016• Total gain in pasture area between 322,702 –385,232 km2.
• Total loss in pasture area between 60,716 -149,361 km2.
• Total gain in cropland area between 119,042 –196,020 km2.
• Uncertainty due to mix class
Is there any field-based evidence?• Arima & Uhl (1995) – Southern Pará
• 44% of smallholder ranchers (~ 250 ha) • 28% of medium-scale ranchers (~ 3,700 ha) sold
properties elsewhere and invested in Amazonia• Richards (2015) – Western Pará
• Ranchers coming from intensive regions• Cattle ranching culture rigidity: ranchers do not switch to agriculture (generational?)
• But still lacking systematic field data collection
Comments/Conclusions• Although focus is in Amazonia, similar process in cerrado areas.
• Land price differentials very likely explains pasture relocation from intensive to extensive frontiers
• Frontier closure?• Agricultural frontier is catching up with cattle ranching
frontier in Amazonia?• Price differentials are narrowing (core #1)?
Comments/Conclusions• Land prices offer several advantages:
• Good proxy for productivity, revenue, and expectations• Price ratios “controls” for economic factors and policies
that affect crop, cattle prices nationally and internationally.• Example: exchange rates• Land prices in many regions is indexed by “sacks of soybean”,
“arroba of cotton”, or “arroba of beef”
Thank You.
Mapbioma Class Aggregation• Forests: dense forest, open forest, mangrove, flooded
forest, degraded forest, secondary forest, silviculture/plantations
• Natural non-forest: natural non-forest, moist non-forest natural, grasslands, other non-forest
• Pasture: pasture, pasture on natural grasslands• Agriculture: annual crops, semi-perennial crops
(sugarcane), mosaic of crops• Class 21: Pasture or agriculture
• If pixel is pasture in latter year, then previous year is pasture if pixel is class 21. Else, class 21
• Allocated to pasture for conservative estimate.
• Other: Else
Source: Mapbiomas. Assumes category Pasture/Cropland is pasture if latter use is pasture
Beef Prices in SP (deflated)
Descriptive stats – pasture land prices• year min max sd
• 2001 75 5579 1024.681• 2002 90 6945 1233.677• 2003 125 8926 1717.597• 2004 186 9520 2137.043• 2005 193 9199 2016.152• 2006 184 8817 1938.113• 2007 192 13350 2343.346• 2008 200 14501 2608.251• 2009 250 14500 2654.941• 2010 270 39500 3575.865• 2011 322 16250 3182.009• 2012 437 22333 4042.665• 2013 625 25833 4667.906• 2014 744 26833 5263.506• 2015 118 27000 5301.911• 2016 600 27000 5558.316
Descriptive Stats – Croplands/Sugarcane prices• year min max sd•
• 2001 35 10992 2125.737• 2002 35 13781 2473.983• 2003 38 17314 3405.549• 2004 50 19571 3799.507• 2005 47 15165 3115.62• 2006 38 14751 3069.308• 2007 40 18483 3773.962• 2008 49 23763 4707.662• 2009 60 24250 4940.909• 2010 60 23500 5095.612• 2011 1517 29250 5246.043• 2012 90 31833 7267.106• 2013 134 38500 8435.402• 2014 358 44667 9653.076• 2015 2500 48417 8970.538• 2016 2775 50875 9544.657•
Soybean prices - deflated•
• year sp pr rs mt ms go•
• 1. 2000 61.888 60.976 61.632 49.76 56.576 55.856• 2. 2001 70.096 67.376 69.12 57.104 63.824 63.872• 3. 2002 84.24 84.432 85.952 70.992 81.344 77.056• 4. 2003 84.624 85.36 83.84 72.592 80.16 77.36• 5. 2004 82.24 82 80.704 71.232 76.944 75.584•
• 6. 2005 53.888 54.848 54.032 46.352 49.296 48.592• 7. 2006 47.328 49.04 45.76 39.28 44.096 44.224• 8. 2007 58.52 57.72 55.4 49.52 55.67 55.41• 9. 2008 71.2 70.05 71.72 61.04 68.28 66.01• 10. 2009 69.86 72.15 71.11 60.29 68.36 64.18•
• 11. 2010 56.71 55.67 57.15 48.64 53.13 52.46• 12. 2011 60.76 59.88 60.27 54.03 57.75 57.29• 13. 2012 79 79.74 78.85 71.68 76.46 75.19• 14. 2013 72.39 74.9 76.62 64.01 70.27 68.94• 15. 2014 70.27 71.21 71.74 62.46 68 67.04•
• 16. 2015 69.33 69.26 72.28 62.4 65.51 67.1•
Speculation• Reservation value• Defined as difference between actual and estimated potential value of land (expectation) –costs to convert land to productive use
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