7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
1/53
Changes in the Agro-Climate Effects on Cereal Crop Yields:Panel Evidence from India (1972-2002)
with Implications for Sub-Saharan Africa
SSD Seminar
e . ,
Takuji W. Tsusaka
Keijiro Otsuka
Copyright 2012 by Takuji W. Tsusaka and Keitjiro Otsuka. Readers may make verbatim copies of this documentfor non-commercial purposes by any means, provided this copyright notice appears on all such copies.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
2/53
Introduction
Modern Varieties (MVs): High-yielding crop varieties suitable for the
regional agro-climate; Especially wheat
Index: 1961=100
The Green Revolution (GR) in Asia
Key Factors for the GR
Growth in agricultural production consistently outpaced population growth, owing to the
Green Revolution (e.g., Otsuka and Kalirajan, 2006).
Changes in Per-cap* Cereal Crop Production (Value-added)
World
Sub Saharan Africa
1
and rice varieties.
Irrigation: Stable and sufficientsupply of water.
Fertilizer: Intensive use of chemicalfertilizer.
Other: Markets (inputs/outputs),infrastructure (e.g. road), credit,
education.
The technological innovation and other complementary factors spurred the agricultural
productivity in Asia, which led to rural poverty reduction (as well as non-farm sector growth).(e.g., Otsuka et al., 2009; Lipton, 2007; Otsuka and Yamano, 2005; Fan et al., 2000)
Source: FAOSTAT* National
South Asia
Southeast Asia
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
3/53
5.0
6.0
Introduction
(Index: 1961=100)
Agricultural Stagnation in Sub-Saharan Africa (SSA)
Staple food production has been increasing in SSA, but the rate of increase is not high enough
and has been exceeded by its population growth.
(tons/ha)
Average Cereal Yields, 3-Year Moving Averages
World
SSA
Changes in Per-cap* Cereal Crop Production (Value-added)
0.0
1.0
2.0
3.0
4.0
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
2
SSA sees a decline in per-capita agricultural production.
Source: Calculation with FAOSTAT Data
* National
or mer ca
Asia
South Asia
North Africa
SSA
100
South
Asia
SoutheastAsia
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
4/53
Why has SSA missed the GR? (1)
Policies
(&Governance)
AgriculturalProductivity
Irrigation (Water
Management)
Markets/Credit/
Infrastructure/Education
High-YieldingVarietiesR&D
Climate
Endowments
Fertilizer
Introduction
3
Critics have long argued that there is limited potential to attain a GR in SSAdue to its adverse climate endowments.
Dry Climate: A number of studies show significant effects of climate on crop yields,
particularly positive effects of rainfall (Seo and Mendelsohn, 2007; Auffhammer et al., 2006; Olesen
and Bindi, 2002; Sanghi et al., 1998; Bruce et al., 1996; Reilly et al., 1996; Adams et al., 1995). Diverse Climate: It results in producing a broad range of staple crops, leading to
limited scale benefit of investing in standard technical packages as in the case of Asia
(Omano, 2003; Mwabu and Thorbecke, 2004).
One of the major constraints in SSA is its unfavorable (i.e.: dry) and diverse climate,
since climate is a direct input for agricultural production (Omano, 2003; Mwabu and Thorbecke,
2004).
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
5/53
Policies
(&Governance)
AgriculturalProductivity
Irrigation (Water
Management)
Markets/Credit/
Infrastructure/Education
High-YieldingVarietiesR&D
Climate
Endowments
Fertilizer
IntroductionWhy has SSA missed the GR? (2)
4
Under-developed Irrigation: irrigation and other water management systems have not
been widely introduced in SSA (e.g. Hayami and Godo, 2005; Spencer, 1994).
Insufficient Fertilizer Use:partly a consequence of high fertilizer prices due to poor
infrastructure (e.g., road), and lack of credit and education.
Other Constraints
The Asian GR technology has been
recognized as dependent on intensive and
controlled supply of water and fertilizers.
The adoption of improved
technologies in SSA has
been confined to limited
regions under favorableconditions.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
6/53
IntroductionAny potential for African agriculture?
Country-specific case studies on African agriculture point out that the rice yields willsignificantly increase once the constraints are properly addressed along with the
adoption of modern technologies (Kajisa and Payongyon, 2008; Sakurai, 2006; Kijima et al., 2006;Diagne, 2006; Goufo, 2008).
The Asian GR has been technology-led, and thus investments in agricultural research
Some recent studies show that there is some potential for new technology adoption and
crop yield improvement in SSA, which has just yet to be effectively exploited.
5
Since all these studies are based on descriptive statistical analysis, a more formal
econometric testing on the subject would confirm this argument.
and extension would lead to growth in African agriculture(Otsuka and Kijima, 2010)
. In India, MVs of cereal crops were introduced in favorable areas at the initial stage of
the GR. But, the MV adoption rate in unfavorable areas started to pick up at the later
stage as technology continued to advance. (Byerlee, 1996; Fan and Hazell, 1999; Janiah et al.,
2005; Gollin, 2004).
Furthermore, In SSA, only
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
7/53
Objective
Conventionally, the Asian GR technology has been recognized as a resource-
demanding technology which relies on the intensive use of water as well as
fertilizers.
The GR technology generally results in aggravating the adverse effect of harsh
agro-climate on crop yields.
-
6
dependence of crop yields, that would make a positive case for the possibility of anAfrican GR.
Itis interesting and important to empirically explore whether and to what extent theinfluence of agro-climatic conditions on crop productivity has augmented or
mitigated by the GR in Asia.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
8/53
Contribution of The Study
The over-time changes in the impacts of agro-climate on crop yields, which have yet to be
unveiled, are examined.
Few studies on the subject have ever employed a panel data set that covers sufficient
observations along cross-sectional, temporal, and crop-wise axes, due to the data constraints.This study uses a crop-by-crop district-level panel over a long period, which has at least three
advantages over the existing studies:
The use of fixed (or random) effect can
7
- control for the unobservable time-invariant district-specific effects, which can mitigate omittedvariable problems
- alleviate estimation biases which may arise, for example, from endogeneity of explanatory variables
and sample selection.
The long-term data set enables the assessment of the over-time changes in the impacts of climatic
conditions.
Yield functions are estimated for each individual crop and are compared with each other, which leadsto finding the comparative advantage of one crop over others in certain production environments.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
9/53
42
8
18
23
12
1
24
9
1
India vs. SSAProportion of Area Harvested to Cereal Crops (%)
21
47
2~3
SSA India Other Asia
2003-2007 Avg.
Sorghum
Millet
70-73 Avg.
India
03-07 Avg.
35
19
8
61
6
3
27
32
10
44
34
Indias diverse cropping patterns reflect its diverse agro-climate.
The agricultural production environments (in some parts of India, if not all)
are similar to those in SSA, which implies a technology transferability.
Source: Calculation with FAOSTAT Data
* Cassava, Teff, Potatos, Ragi, Oats, Barley and other
Other*
Maize
Wheat
Rice
Source: Calculation with CMIE Data
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
10/53
Bajra (Pearl Millet) Field in India
9
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
11/53
1.5
2.0
2.5
3.0
3.5
4.0
India
(tons/ha)
Southeast Asia
Combined Cereal Yields (3-year MA): India vs. SSA
India vs. SSA
10
0.0
0.5
1.0
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
Source: Calculation with FAOSTAT Data
Sub-Saharan Africa
Despite the less favorable production environments, cereal crop yield in SSA was not significantly
inferior to that in India until the early 80s.
Today there is a gap of two-fold in cereal yield.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
12/53
2.5
3.0
3.5
2.5
3.0
3.5
Wheat
Rice
Maize
Yield (tons/ha) Yield (tons/ha)
Wheat
India Sub-Saharan Africa
Cereal Yields (3-year MA) by Crop: India vs. SSA
India vs. SSA
11
0.0
0.5
1.0
1.5
.
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
0.0
0.5
1.0
1.5
.
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
Despite the much more favorable economic and climatic conditions in India, the yields for
sorghum and millet are almost the same in both regions, indicating a limited
transferability of the technology from India to SSA.
SorghumMillet
RiceMaize
Sorghum
Millet
Source: Calculation with FAOSTAT Data
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
13/53
Wheat
India vs. SSACereal Yields by crop and their growth: India vs. SSA
Yields (tons/ha) Growth
(times)
India
Yields (tons/ha) Growth
(times)
SSA
61-63 Avg. 05-07 Avg. 61-63 Avg. 05-07 Avg.
0.7 2.1 3.10.8 2.5 3.0 1.2
India Yield
SSA Yield
05-07 Avg.
12
The difference in current rice yield is huge, followed by maize. Room for the transfer of rice and maizetechnology?
When it comes to sorghum and millet, there would be limited transferability of technology from Asia to SSA.
In SSA, as far as the yield growth rate is concerned, a GR seems to be occurring in wheat, but not as much inthe other crops.Possible to expand the wheat area?
Maize
Millet
Sorghum
1.3 1.8
1.41.0 1.6 1.5
0.8 1.0 1.4
0.6 0.9 1.5
1.5 3.32.1
1.1 2.3 2.1
0.5 0.8 1.7
0.4 0.9 2.1
1.91.4
0.8
1.0Source: Authors calculation with FAOSTAT Data
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
14/53
Limitation of Wheat Expansion in SSAWheat production map of the world
(Average percentage of land used for wheat productiontimes average yield in each grid cell)
Temperate Zone and SSA
13
Source: Compiled by the University of Minnesota Institute on the Environment with data from: Monfreda, C., N.Ramankutty, and J.A. Foley. 2008. Farming the planet: 2. Geographic distribution of crop areas, yields,physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles 22: GB1022
Wheat can be grown well only under acool climate, which is associated with thetemperate climate zone. In the Africancontinent, the temperate climate zone isfound only in limited part.
Wheat is thus grown only in the Republicof South Africa, the highlands in Ethiopia,and a few other regions, which is matchedwith the mere 3 percent of the total croparea planted to wheat.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
15/53
India vs. SSA
Evolution of Cropping PatternsArea Harvested by Cereal Crop
Other
Maize
Sorghum
Millet
Million haAAGR
(%)
-4.00.8-1.7
-1.3
India
Other
Wheat Rice
AAGR
(%)
0.5
1.2
2.6
-0.5
SSAMillion ha
14
Source: Calculation with FAOSTAT Data
In India, the three GR crops seem to be
replacing Millet and Sorghum.
Wheat
Rice
1.6
0.5
Millet
Sorghum
Maize
1.2
1.5
In SSA, all crops except wheat are
spreading. In particular, rice recently.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
16/53
India in Focus:
Diffusion of Irrigation and Modern Varieties
0.6
0.7
0.8
0.9
1.0
Proportion of Irrigated Area by Crop
Wheat
Rice
Wheat
Rice
Maize
Millet
Sorghum
Proportion of Area Sown to MVs by Crop
0.6
0.7
0.8
0.9
1.0
15
-
0.1
0.2
0.3
0.4
0.5
1970 1975 1980 1985 1990 1995 2000 2005
The irrigation coverage varies largely by crop, and
it has not been increasing considerably overtime.
Source: CMIE Database
Maize
MilletSorghum0.0
0.1
0.2
0.3
0.4
0.5
1970 1975 1980 1985 1990 1995 2000
There has been a rapid increase in area planted to
MVs, even for sorghum and millet in recent years,though their yields are not growing.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
17/53
India in Focus:
Agro-Climate and Crop Choice
Temperature
()
Rainfall
(mm)
1998-2002 Five-Year Average
Millet
26.2
794
Sorghum
26.3
848
Maize
25.6
863
Rice
25.5
1,007
Wheat
22-23
852
16
Millet and Sorghum are grown in drier and slightly warmer environments.
Sources: India Water Portal; CMIE Database
Irrigation(%)
Yield
(kg/ha)
# Districts
18
1,001
269
14
821
258
34
1,825
327
58
2,007
412
79
2,153
356
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
18/53
Data Source: IndiaDistrict-Level Panel Data Construction Covering ~600 Districts
Variable Raw Data
Agricultural Output (by crop)Yield (by crop)
Area Sown (by crop) CMIE
CMIE
Source
ClimateTemperature
Rainfall CMIE
India Water Portal of the MD
17
The database is composed of five different sources,
including private research corporations: CMIE and Datanet India.
Commonly Available Years :1972-2002Notes: CMIE = Center for Monitoring Indian Economy Pvt. Ltd.,
MD = India Meteorological Department, X.Zhang@IFPRI , K. Kumar@WB
Irrigated Area (by crop)
MVs Adoption Rate (by crop)Technology (by crop)
CMIE
(Not Available on District Level)
Literacy RateControls
Population Density
X. Zhang, Datanet India
K. Kumar, Datanet India
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
19/53
Econometric Approach
Eliminate Sample Selection Bias by 2-Step Estimation (Heckman, 1979)
18
The inverse Mills ratio is calculated using the result of the probit estimation.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
20/53
Econometric Approach
19
Since consistent data on technology (MV adoption rate and other) are unavailable on districtlevel, it is assumed that theyear dummies and the time trend variables capture the impacts of
technology.
The interaction terms between explanatory variables and time trend variables (e.g., Xt , Xt2)
are meant to examine whether there have been over-time changes in the impacts of climate and
other explanatory variables due to any technological change.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
21/53
Theoretical Framework
Yield The marginal effect is the slope of
tangent on the yield curve.
It may differ from place to place.
It changes when agricultural
technology changes
Yield Function
1) Climate Effects on Crop Yield
20
ma e ar a e e.g. ra n a , empera ureflooddrought
Climate Variable (e.g. rainfall, temperature)
Yield
TV
Early MV
Newer MV
Researchers claim that early generations
of MVs are resource-demanding and
sensitive to harsh agro-climate. How about newer MVs?
Interesting to examine the changing
impacts
2) Changes in Climate Effects
?
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
22/53
Regression Results for India, 1972 to 2002
Rice Dependent Variable: Rice Yield (Ln) Estimated Coefficients on Selected Explanatory Variables:
Temperature
Coefficients
Temp
Temp t Temp t2
TempIrri
Explanatory Variable
Rainfall
Rain all t
***
***
***
0.1122
-0.00440.0001
-0.0684 ***
0.5828
-0.0234
***
**
Variable
Yield
Yield Function(Initial)
21
The positive impacts of temperature, rainfall, irrigation are found, which indicates the upward slopingpart of the yield function of each variable.
The result for population density is supportive of the induced innovation hypothesis of Hayami andRuttan (1985) which states that as population increases, increasing scarcity of land induces thedevelopment and di usion o land-saving and yield-enhancing technologies.
Statistical significance: *10%, **5%, ***1%
Irrigation
Coverage
PopulationDensity
Rainfall t2
RainfallIrri
Irri
Irri t Irri t2
PopDen
PopDen t PopDen t2
***
*
0.0001-0.1563
3.1820
0.0132
-0.0005
0.0914-0.0014
0.0001
***
**
***
1 C 11 %
1% pt. 3.2 %
1 % 0.09 % (elasticity=0.09)
1 % 0.58 % (elasticity=0.58)
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
23/53
Regression Results for India, 1972 to 2002
Rice Dependent Variable: Rice Yield (Ln) Estimated Coefficients on Selected Explanatory Variables:
Temperature
Coefficients
Temp
Temp t Temp t2
TempIrri
Explanatory Variable
Rainfall
Rain all t
***
***
***
0.1122
-0.00440.0001
-0.0684 ***
0.5828
-0.0234
***
**
Time
Marginal Effect
(=Dependence)
Avg. over
the period
22
The impact of climatic variables decreases over time (at a diminishing rate):
The predicted irrigation effect (%/% pt.) increases over time but slows down: 3.2 (72) 3.5 (86) 3.6 (02)
Statistical significance: *10%, **5%, ***1%
Irrigation
Coverage
PopulationDensity
Rainfall t2
RainfallIrri
Irri
Irri t Irri t2
PopDen
PopDen t PopDen t2
***
*
0.0002-0.1563
3.1820
0.0132
-0.0005
0.0914-0.0014
0.0001
***
**
***
Time
Marginal Effect
(=Dependence)
Avg. over
the period
*
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
24/53
Regression Results for India, 1972 to 2002
Rice Dependent Variable: Rice Yield (Ln) Estimated Coefficients on Selected Explanatory Variables:
Temperature
Coefficients
Temp
Temp t Temp t2
TempIrri
Explanatory Variable
Rainfall
Rain all t
***
***
***
0.1122
-0.00440.0001
-0.0684 ***
0.5828
-0.0234
***
**
Irrigation
Marginal Effect of Climate
(=Dependence on Climate)
23
It is indicated that irrigation can reduce the dependence of rice yield on climatic factors, to some extent.
The over-time changes in the impacts of climate are distinct from the influence of irrigation diffusion, since thatinfluence is controlled for by the climate-irrigation interaction terms.
Therefore, the critically important finding is thatthe dependence of rice yield on climate mitigated over timeregardless of the availability of irrigation, which cannot be understood without considering the impact of theadoption of MVs with shorter maturity and drought-tolerance traits.
Statistical significance: *10%, **5%, ***1%
Rainfall t2
RainfallIrri 0.0001-0.1563 ***
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
25/53
Regression Results for India, 1972 to 2002
Wheat, Maize, Sorghum, and Millet Dependent Variable: Ln Yield Estimated Coefficients on Selected Explanatory Variables:
Temperature
Wheat Maize
**
***
***
Temp
Temp t Temp t2
TempIrri
Explanatory Variable
Rainfall
Rain all t
-0.0162
-0.00310.0000
0.0456 ***
0.3209
-0.0187
***
*
-0.0662
0.0085-0.0002
0.0403 *
0.0709
-0.0015
***
***
Sorghum Millet
*
***
***
0.0401
-0.00210.0001
-0.0305
0.5066
-0.0333
***
0.0561
0.0030-0.0001
-0.1305 ***
0.2750
-0.0027
*
24
The impacts of climatic variables on crop yields decreased over time at a diminishing rate in severalcases. At least, in no single case, the impact of climate augmented.
Irrigation leads to a reduced climate dependence of crop yields. Induced innovation hypothesis is supported in all crops in recent years at least.
Statistical significance: *10%, **5%, ***1%
***
Irrigation
Coverage
***PopulationDensity
Rainfall t2
RainfallIrri
Irri
Irri t Irri t2
PopDen
PopDen t PopDen t2
0.0004
-0.1291
-0.0698
-0.0018
-0.0003
0.11560.0009
0.0000
***
***
0.0000
0.0011
-0.9403
0.0051
-0.0003
-0.21120.0127
-0.0001
***
***
0.0007
0.1191
0.4155
-0.0619
0.0017
0.1473-0.0009
0.0000
**
**
*
-0.0002
0.0782
3.2329
-0.0207
0.0004
0.08820.0090
-0.0003
**
***
***
***
***
**
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
26/53
Concluding Remarks
(1) Summary of the Findings
From the Descriptive Statistics
The gap in aggregate cereal yield between Asia and SSA was so minor until the early 1980s
despite the much more favorable climatic, economic, and political conditions in Asia. The yield diversion occurred due to the adoption of improved technology in Asia and the
failure of that in SSA. In other words, the Asian GR is likely to be a technology-led revolution.
The Asian GR technolo ies were develo ed rinci all for wheat and rice followed b maize. In
25
fact, Indian farmers have been steadily replacing the lower-yielding crops (sorghum and millet)
by the higher-yielding crops, which is one of the reasons why the compound cereal yield has
been growing in India.
Given the absence of the yield difference for sorghum and millet between Asia and SSA eventoday, the technology transferability from Asia to SSA for these two crops seems to be absolutely
limited.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
27/53
Concluding Remarks
(1) Summary of the Findings From the Regression Results
The impacts of climatic conditions (temperature and rainfall) on crop yields have reduced overtime, due to the adoption of MVs and associated technologies.
i. The impact of temperature, whether the average is positive or negative, declined for rice
and maize.
ii. The rainfall effect mitigated for wheat, rice, and sorghum.
The traits of MVs have contributed to alleviating, not aggravating, the influence of climatic
conditions, which is in contrast with the conventional notion that MVs are typically
resource-demanding and are higher-yielding only under favorable environments.*
26
*A possible reason is that the short maturity varieties can grow up in a shortened period during which rainfall is assured. It is also likely that improved droughttolerance of MVs reduces the downward yield risk, which leads to a decrease in the marginal effect in the low range of rainfall.
Role of Irrigationi. Rice MVs require more irrigation water than do TVs. Interestingly enough, the rate of
increase in irrigation effect is relatively large in the initial phase of the GR, but slows
down in the later phase.
ii. Irrigation works to mitigate the influence of climate endowments on crop yields.
The induced innovation hypothesis proposed by Hayami and Ruttan (1985) is broadly supported. Continued population pressure is likely to have increased the relative profitability of land-saving
and yield-enhancing technologies along the lines of the hypothesis.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
28/53
Concluding Remarks
(2) Policy Implications It is highly desirable to reverse the declining trend in the investment in international agricultural
research activities, to enhance agricultural productivity in regions with unfavorable climates
including SSA.
Facing a tight budget for international agricultural research, crop-wise foci would be necessaryto clarify policy priorities.
i. Rice: A critically important implication of this study should be a focus on rice as astrategic crop in SSA, because of the abundant evidences of improved resistance to harsh
climate, and the large gap in current yield between Asia and SSA, indicating an
27
opportunity to transfer the Asian technology.
ii. Maize: Since maize is the most widely cultivated crop in SSA, the productivity of maize
farming must be enhanced. The advantage of maize crop is that the yield is not adversely
affected by the unavailability of irrigation, meaning that maize has comparative advantage
in rain-fed farming systems. Therefore, maize can be the second strategic crop after rice.
Yet, it must be recognized that unlike rice, the maize technology developed in Asia is not
conducive to weakening the impact of drought on maize yield.iii. Wheat: The limitation of wheat area expansion in SSA requires due attention in spite of its
outstanding yield growth in the region.
iv. Sorghum and Millet: There exists no difference in yield at all between Asia and SSA.
Technologies for sorghum and millet in SSA are unlikely to be developed from the
experiences in Asia.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
29/53
Concluding Remarks (Contd)
(2) Policy Implications Try to switch from low-performing crops (sorghum and millet) to high-performing crops (wheat,
rice, and maize), as it has been a driver for achieving growth in overall crop productivity in India.
Also in SSA, by the mid-20th century, maize had immigrated and replaced much of sorghum and
millet fields in Eastern and Southern Africa, partly because maize yielded more grain (Anthony1988).
Whether TVs or MVs, crop shift from sorghum and millet to rice and maize, wherever
applicable, is strongly suggested for fostering the agricultural productivity growth in SSA.
28
nvestment n rr gat on can e an e ect ve measure or tac ng ars agro-c mate en owments
in SSA, as well as the looming threat of climate change.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
30/53
Thank you very much for listening.
29
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
31/53
Appendix
30
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
32/53
Introduction
Importance of Agricultural Productivity in Developing Countries
PovertyReduction
Economic
Growth
Agricultural
Productivity
Quotes: Food Security
31
Agricultural productivity plays a critical role in
economic growth, poverty reduction, food security in developing countries.
3/4 of the poor in sub-Saharan Africa live in rural areas where agriculture is a dominant sector.(WDR, 2008)1 % decrease in agricultural GDP leads to a decrease in consumption of the three poorest decile
groups by 4-6 %. (Ligon and Sadoulet, 2007)
33 % of the economic growth in sub-Saharan Africa from 1990 to 2005 comes from the
agricultural sector. (WDR, 2007)
1 % increase in agricultural GDP leads to an increase in expenditure of the poorest deciles by
>2.5 percent. The effect is superior to that of non-farm income. (Christiansen and Demery, 2007)
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
33/53
Policies(&Governance)
AgriculturalProductivity
Irrigation (Water
Management)
Markets/Credit/
Infrastructure/Education
High-YieldingVarietiesR&D
Climate
Endowments
Fertilizer
Introduction
Why has SSA missed the GR? (3)
32No hope for agriculture in SSA?
Public spending on African agriculture, including R&D, has fallen to the record low of
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
34/53
1) Impacts of Climate Endowments on Crop Productivity
Contribution of this Study
The changes in the impacts of climate endowments have yet to be well known since the
dynamic evidence has been scanty.
1) The (static) impacts of climate on agricultural crop yields. Somewhat known
2) The over-time changes in the impacts of climate endowments. Not well known
Agronomic Yield Function Approach (a.k.a. Crop Modeling)
33
Method: Specific crops experience differing climate in laboratories. Then, Yields Datavs. Climate Data (temperature, precipitation, etc.) are collected.
Shortcoming: Bias (i.e., unlikely to reflect possible adjustments by farmers)
Result: Sensitive to Climate (e.g., Mendelsohn et al., 1994)
Cross Sectional Regression (a.k.a. Ricardian Approach)
Method: Empirically regress crop productivities (e.g., land prices) on climate
variables, plus other controls.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
35/53
Contribution of this Study
Cross Sectional Regression (Contd)
Result: Not as sensitive as in crop modeling approach.
Mostly on developed countries due to the data availability:
U.S. (Adams et al., 1995; Mendelsohn et al., 1994)
Other developed countries (Olesen and Bindi, 2002; Bruce et al.,
1996; Reilly et al., 1996)
34
Developing countries:
India and Brazil (Seo and Mendelsohn, 2007; Sanghi et al., 1998).
Negative impact of temperature and rainfall
Shortcoming: Omitted variable problems (e.g., unobservable skills of farmers, soil
quality) which could generate a bias of unexplained sign or magnitude.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
36/53
Contribution of this Study
Panel Data Approach
Method: District Fixed effect/Random effect is controlled for.
Shortcoming: Often unfeasible due to the data constraints, especially for developing countries.
Result: ambiguous or negative impact of temperature
US county-level analysis: Deschnes and Greenstone (2007), Schlenker and Roberts (2006)
India state-level analysis: Auffhammer et al. (2006)
35
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
37/53
Contribution of this Study
Another Unique Aspect: Asia-Africa Comparison.
Although the quality and availability of data are inferior for the African study, the direct
comparison can assess the difference in the progress of technology adoption in the two regions.
The choice of India Similarities in agriculture between India and SSA
Diversity in agro-climate, resulting in similar cropping patterns.
Differing poverty incidence.
36
Relatively small average farm size.
Although there are signs of hope documented in some case studies on agricultural technological
situations in African countries (Diagne, 2006; Sakurai, 2006; Goufo, 2008; Kajisa and Payongayong, 2008;
Kijima et al. ,2006), the real challenge is to translate individual successes into sustainable and
systematic improvements in agricultural performance, which facilitate the identification of policy
priorities.
In order to achieve this goal, it is important to accumulate hard evidence to design appropriate
development strategies (Otsuka and Kijima, 2010).
This study, therefore, is expected to provide positive evidence through solid econometric analyses.
India in Focus:
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
38/53
India in Focus:
Irrigation and Crop Yields
Temperature
()
Millet
26.1 25.7
Irrigation Coverage
Low High
Sorghum
26.2 25.4
Irrigation Coverage
Low High
Maize
25.1 26.1
Irrigation Coverage
Low High
Rice
24.6 25.9
Irrigation Coverage
Low High
Wheat
23.4 25.7
Irrigation Coverage
Low High
1998-2002 Picture
37
Clearly, irrigation coverage is higher in rain scarce districts. Even under dry climates, irrigation
boosts the yields largely for wheat and rice, but not as much for the other crops.
Source: India Water Portal; CMIE Database
Rainfall
(mm)
Yield
(kg/ha)
# Obs for
crop yield
Irrigation Coverage = % of Sown Area for each crop
High > 50 %; Low < 50 %
811
964
228
701
1,207
41
877
836
224
658
720
34
895
1,630
215
802
2,198
112
1,127
1,418
178
920
2,455
234
1,045
1,203
65
809
2,365
291
Note: There are observations for which irrigation coverage is unknown
India in Focus:
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
39/53
India in Focus:
Irrigation and Crop Yields
Temperature
()
1971-1975 Picture
25.7 26.6
Low High
Millet
Irrigation Coverage
25.8 24.9
Low High
Sorghum
Irrigation Coverage
25.4 25.3
Low High
Maize
Irrigation Coverage
25.2 25.6
Low High
Rice
Irrigation Coverage
25.2 25.3
Low High
Wheat
Irrigation Coverage
38
Source: India Water Portal; CMIE Database
Rainfall
(mm)
Yield
(kg/ha)
# Obs for
crop yield
Looking back at the early 70s, the role of irrigation did not seem as crucial as in the late 90s.
Irrigation Coverage = % of Sown Area for each crop
High > 50 %; Low < 50 %
Note: There are observations for which irrigation coverage is unknown
916
522
145
801
752
7
961
568
159
735
601
10
1,032
1,041
146
898
1,276
41
1,138
858
130
963
1,368
75
1,134
1,036
100
868
1,430
111
D S SSA
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
40/53
Data Source: SSACountry-Level Panel Data Construction
Variable Raw Data
Agricultural Output (by crop)Yield (by crop)
Area Sown (by crop) FAOSTAT
FAOSTAT
Source
ClimateTemperature (TBR)
Rainfall (TBR) GOSIC
GOSIC
39
The database combines data from four public sources: Technology variables are unavailable over the
long period. Moreover, the database has many missing observations across countries.
The database covers :1967-2004Notes: GOSIC = The Global Observing Systems Information Center of the U.S.
TBR = To be replaced by new data.
Prices (by crop) Nominal Price of OutputDeflator
FAOSTATWDI
Literacy RateControls
Population Density (TBR)
UNESCO
WDI
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
41/53
Approach: SSA
Step 1 is dispensed with
40
For SSA, the sample selection model does not work out, probably because the number ofcross-sectional observations is not large(~30) and each crop is grown in many of those
countries. Thus, I directly perform the outcome estimations assuming that the biases are
negligible.
Otherwise, the methodology is largely the same as in the case of India, except that there
is no data for irrigation, and price is available.
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
42/53
Approach (Contd)
Consideration on Endogeneity
Iijt= Irrigated land area for crop i divided by total area sown to crop i (India)
Again, instruments are absent. However, in the early stage of the GR, most of the irrigation wasgravity irrigation which was installed by the public sector. Therefore, irrigation can be consideredfairly exogenous especially in the early stage.
District-s ecific effect model ma mitigate, if not eliminate, the endogeneit bias because irrigation
41
investment can be determined based on time-invariant factors such as district-specific geography
and environment.
It is assumed that the endogeneity of these variables is not serious in this analysis.
h ( d)
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
43/53
Approach (Contd)
For SSA: Two Specifications for Yield Functions
[Model 1] With Year Dummies (from 1968 to 2004, 1967 as the base year) Without Time Trend Variables
Two-way Fixed Effect Model
To absorb the average yearly change in yield that is not explained by the explanatory variables
42
Aggregate macroeconom c an c mat c s oc s
Overall technological improvements
[Model 2] Without Year Dummies
With Time Trend Variables; tand t
2
(t= 0 for 1967)
To capture the trend in general technological improvement and its acceleration (or deceleration) whichis not picked up by the interaction terms (Xtand Xt2).
Four specifications for SSA: M1 w/o P; M2 w/o P; M1 w/P; M2 w/P
Fixed Effect Regression Results for SSA, 1967 to 2004
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
44/53
Fixed Effect Regression Results for SSA, 1967 to 2004
Wheat
Dependent Variable: Wheat Yield (Ln) Estimated Coefficients on Selected Explanatory Variables:
Temperature
Model 1
Temp
Temp t Temp t2
Explanatory Variable
0.2699
-0.0229
0.0004
*0.0670
-0.0077
0.0001
0.4518
-0.0277
0.0004
0.1445
-0.0091
-0.0001
Model 2 Model 1 Model 2
Without Price With Price
***
***
***
***
***
***
*
**
**
Model 1: Year Dummies
Model 2: Time Trend
43
The impact of temperature is positive but decreases over time (at a diminishing rate).
The impact of rainfall is almost insignificant.
The effect of population density is initially very significantly positive, which is supportive of the inducedinnovation hypothesis. But the effect weakens over time. Exhaustion of technology?
Statistical significance: *10%, **5%, ***1%
Rainfall
Population
Density
Rainfall
Rainfall t Rainfall t2
PopDen
PopDen t
PopDen t2
-0.2229
0.0149
-0.0001
1.6184
-0.1279
0.0017
*
***
-0.1300
0.0054
0.0001
1.3718
-0.0913
0.0010
*
*
***
-0.4205
0.0426
-0.0007
0.1473
-0.0009
0.0000
-0.1841
0.0199
-0.0003
0.0882
0.0090
-0.0003***
**
***
***
** ***
***
***
***
***
Fixed Effect Regression Results for SSA, 1967 to 2004
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
45/53
g ,
Rice
Dependent Variable: Rice Yield (Ln) Estimated Coefficients on Selected Explanatory Variables:
Temperature
Model 1
Temp
Temp t Temp t2
Explanatory Variable
-0.1485
0.0136
-0.0002
-0.1339
0.0111
-0.0002
-0.1425
0.0136
-0.0003
-0.1154
0.0103
-0.0002
Model 2 Model 1 Model 2
Without Price With Price
**
***
***
*
***
***
*
***
***
Model 1: Year Dummies
Model 2: Time Trend
**
***
***
44
The declining impacts of climate are found for rainfall as well as temperature.
The effect of population density is very significantly positive, which is supportive of the induced
innovation hypothesis. Unlike wheat, the effect increases over time (at a diminishing rate).
Statistical significance: *10%, **5%, ***1%
Rainfall
Population
Density
Rainfall
Rainfall t Rainfall t2
PopDen
PopDen t
PopDen t2
0.6256
-0.0413
0.0006
1.4199
0.0531
-0.0012
***
***
0.5375
-0.0310
0.0005
1.6446
0.0413
-0.0010
**
0.3229
-0.0200
0.0003
1.9960
0.0460
-0.0011
0.1102
0.0005
0.0000
2.0197
0.0334
-0.0009***
**
***
***
** **
***
**
***
***
***
**
*
*
Fixed Effect Regression Results for SSA, 1967 to 2004
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
46/53
g ,
Maize
Dependent Variable: Maize Yield (Ln) Estimated Coefficients on Selected Explanatory Variables:
Temperature
Model 1
Temp
Temp t Temp t2
Explanatory Variable
-0.0237
-0.0015
0.0000
*-0.0001
-0.0051
0.0001
0.0815
-0.0093
0.0001
0.0594
-0.0095
0.0001
Model 2 Model 1 Model 2
Without Price With Price
***
*
***
**
Model 1: Year Dummies
Model 2: Time Trend
45
The declining impacts of rainfall is found.
Unlike wheat and rice, the effect of population density is mostly insignificant.
Statistical significance: *10%, **5%, ***1%
Rainfall
Population
Density
Rainfall
Rainfall t Rainfall t2
PopDen
PopDen t
PopDen t2
0.6656
-0.0560
0.0011
0.1530
0.0225
-0.0004
0.5151
-0.0371
0.0007
0.3365
0.0101
-0.0001
0.1644
-0.0136
0.0004
-0.3977
-0.0076
0.0002
0.1574
-0.0073
0.0002
-0.7004
-0.0076
0.0002
**
*
***
***
******
***
***
Fixed Effect Regression Results for SSA, 1967 to 2004
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
47/53
g ,
Sorghum
Dependent Variable: Sorghum Yield (Ln) Estimated Coefficients on Selected Explanatory Variables:
Temperature
Model 1
Temp
Temp t Temp t2
Explanatory Variable
-0.1902
0.0060
-0.0001
-0.1392
0.0017
0.0000
-0.1138
0.0012
0.0000
-0.1041
-0.0002
0.0000
Model 2 Model 1 Model 2
Without Price With Price
***
*
* *
Model 1: Year Dummies
Model 2: Time Trend
***
46
The declining impact of temperature is found in Model 1 without price.
The impact of rainfall is totally insignificant.
The induced innovation hypothesis is not supported for sorghum in SSA.
Statistical significance: *10%, **5%, ***1%
Rainfall
Population
Density
Rainfall
Rainfall t Rainfall t2
PopDen
PopDen t
PopDen t2
0.1685
-0.0081
0.0000
-1.7672
0.0023
0.0001
0.0415
0.0064
-0.0002
-1.5738
-0.0122
0.0004
***
0.2404
-0.0065
0.0000
0.5120
-0.0486
0.0009
0.1634
0.0077
-0.0003
0.4091
-0.0529
0.0010**
*** ***
***
***
***
Fixed Effect Regression Results for SSA, 1967 to 2004
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
48/53
g
Millet
Dependent Variable: Millet Yield (Ln) Estimated Coefficients on Selected Explanatory Variables:
Temperature
Model 1
Temp
Temp t Temp t2
Explanatory Variable
-0.1822
0.0057
-0.0001
-0.0986
0.0024
0.0000
0.0405
-0.0108
0.0001
0.0818
-0.0102
0.0001
Model 2 Model 1 Model 2
Without Price With Price
**
**
*
**
Model 1: Year Dummies
Model 2: Time Trend
47
The declining impacts of climate is not found for millet in SSA.
The impact of rainfall is totally insignificant.
The induced innovation hypothesis is not supported for millet in SSA.
Statistical significance: *10%, **5%, ***1%
Rainfall
Population
Density
Rainfall
Rainfall t Rainfall t2
PopDen
PopDen t
PopDen t2
0.2547
-0.0217
0.0005
-0.1933
-0.0092
0.0002
*
0.1610
-0.0132
0.0004
-0.0358
-0.0172
0.0004
*
0.1788
-0.0266
0.0007
-0.4396
0.0084
-0.0001
0.0360
-0.0101
0.0004
-0.2293
-0.0032
0.0002*
**
I I A ll T h l Th M d?
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
49/53
Is It Actually Technology That Mattered?
The results strongly indicate that the impact of climatic factors on crop yields have declined over time,
for wheat, rice, maize, and sorghum, after the irrigation effects are controlled for (in India).
Although it seems reasonable to assume that technological progress represented by the adoption of high-
yielding MVs and other improved production practices has contributed to these over-time changes, it is
not directly proven by the regression analyses since the time trend variables can reflect the effects of a
variety of factors including infrastructure, among other things.
The difficulty is that technology variables, such as MV adoption rate, are unavailable at the district level
48
n t e case o n a. oreover, even t ose var a es were ava a e, t e r use wou enta a pro em o
endogeneity bias, which would not be easy to correct for.
One attempt to obtain a more direct evidence of the impact of technology is to use irrigation as a proxy
for the compound effects of irrigation and MVs if the correlation between irrigation rate and MV adoption
rate is high.
Thus, I propose to investigate the relationship between MV adoption rate and irrigation rate using the
state-level data.
I It A t ll T h l Th t M tt d? (C td)
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
50/53
Coefficient of Correlation between Modern Variety Adoption Rate and Irrigation Rate,
State-wise, by Crop, Three-Year Moving Averages
Is It Actually Technology That Mattered? (Contd)
49
Source: Authors calculation with data from Indiastat and Center for
Monitoring Indian Economy.
Period Wheat Rice Maize Sorghum Millet
1974-1988 0.76 0.79 0.62 -0.27 0.30
1989-2002 0.38 0.35 0.46 -0.08 0.09
This trend is supported by preceding studies by Janaiah et al.(2006), Gollin (2006), and Byerlee (1996),
stating that MVs were adopted primarily in irrigated areas in the early phase of the GR.
Use district-level irrigation rate for wheat, rice, and maize in the early phase of the GR, as a proxy
for district-level MV adoption rate.
Regression with the Proxy Variable: India
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
51/53
Regression with the Proxy Variable: India Dependent Variable: Ln Yield
Time Period: 1974 to 1988
Estimated Coefficients on Climate Variables:
Temperature
Wheat Rice
***
***
Temp Temp t TempIrri (Tech)
Explanatory Variable
-0.0211-0.0057
0.0440
***
***
0.1135-0.0048
-0.0635
***
Maize
*0.0707
-0.0017
0.0182
Irrigation = Technology Indicator
50
It is confirmed that the rainfall elasticity of rice yield decreases by 0.0037 when the MV adoption rateincreases by 1 percentage points.
Difficulty: Early generations of MVs may be more resource-demanding.
Statistical significance: *10%, **5%, ***1%
Rainfall ***
Irrigation
Coverage
Rainfall
Rainfall t RainfallIrri (Tech)
Irri (Tech)
Irri (Tech) t
.
-0.0141
0.0635
-0.3754
-0.0083
.
-0.0046
-0.3741
4.3988
0.0059
**
.
0.0083
-0.0071
-0.4557
0.0171 **
***
***
Concluding Remarks (Contd)
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
52/53
Concluding Remarks (Cont d)
(3) Remaining Issues
Variables that directly represent technology adoption are missing in the analyses.
i. Only the state level MV adoption rate is available in Indias descriptive statistics.
ii. The MV adoption rate, even if it is available, does not express the quality of the MVs, and
thus, does not reflect the continuous improvement in the traits of MVs. The MV
adoption rate may understate the actual effect of available technology on crop yields.
51
. e mpact o s o sorg um an m et on y e s s unc ear s nce t e recent y surg ng
MV adoption rates for these two crops do not lead to the yield growth apparently.
TheMV adoption rate, in this sense, may overstate the actual effect of available technology.
Finding a much more refined indicator of technology would produce more reliable results.
The regressions employed in the analyses are not weighted regressions: i.e., all the districts in
India, larger ones and smaller ones, are treated with equal importance. So are all the countries inSSA. Since there are major and minor districts and countries, it may be preferable to contrive a
measure to take some weighting factor into account, especially for SSA where countries of a
range of economic sizes are included.
Concluding Remarks (Contd)
7/31/2019 Changes in the Agro-Climate Effects on Cereal Crop Yields: Panel Evidence from India (1972-2002) with Implications for Sub-Saharan Africa
53/53
Concluding Remarks (Cont d)
(3) Remaining Issues
The quality and availability of the data for SSA have to be improved if possible.
i. The number of cross-sectional observations is limited and many small countries are left
out of the regressions.
ii. The price variable employed in the SSA analyses should be refined, in terms of both
quality and availability.
52
iii. Another suggestion may be using rural population density in place of national population
density in the country as a whole, since most of agriculture is undertaken by rural farmers.
Throughout this study, the agricultural productivity is expressed in terms of the physical cropyield. Although it would be a daunting task, expressing the productivity in monetary term or
using total factor productivity, instead of physical crop yield, may be an intellectually
stimulating challenge.