Environmental Economics Seminar ... conditional income for each farm type (Seo and Mendelsohn 2007a). ... 1994; 1996; 1999; 2001; Sanghi 1998; Seo et al.
Draft Environmental Economics Seminar Fall 2007 Yale University
A Structural Ricardian Analysis of Climate Change Impacts and Adaptations in South American Farms1
Robert Mendelsohn2 and S. Niggol Seo3*
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Abstract This paper measures the consequences of climate change by estimating a farm model that treats the choice of crops, livestock, and irrigation as endogenous.
The model is
composed of a multinomial choice of farm type and irrigation, and a set of conditional land value functions.
The model is estimated across over 2000 farmers in South
America, a continent prone to severe climate damages.
The results reveal that farmers
currently adapt their choice of farm type and irrigation to climate.
The model predicts
that as climate warms, farmers will shift to mixed crop-livestock and livestock farms and as it dries, they will add irrigation.
Farmers will make these shifts because of
underlying changes in income across farm types as climate changes.
Although it is
likely that farmers will adapt, South American farmers will remain vulnerable to severe climate change scenarios.
JEL Keywords: Agriculture, Climate Change, Adaptation, South America, Valuation. JEL Codes: Q12, Q51, Q54.
I. Introduction
This paper develops a two-stage farm model which estimates which farm type a farmer will choose given local conditions in the first stage, and then estimates conditional income for each farm type (Seo and Mendelsohn 2007a). The model uses cross sectional evidence to reveal both farm adaptations and levels of income.
It is a
blend of earlier literature on the adoption of technology and farm types and the traditional Ricardian method. The paper builds on the agriculture literature on technology adoption (Caswel and Zilberman 1986; Dinar and Yaron 1990; Negri and Brooks 1990; Dinar and Zilberman 1991; Dinar, Campbell, and Zilberman 1992) to explore how adoption is related to climate and the traditional Ricardian model (Mendelsohn et al. 1994).
The
paper estimates this model in South America, a continent where climate change is expected to have serious impacts on agriculture (Downing 1992; De Siqueira et al. 1994; Hofstadter et al. 1997; Magrin et al. 1997; Rosenzweig and Hillel 1998; Mendelsohn, Dinar, and Sanghi 2001).
There are two alternative methodologies in the economics literature that have measured the agricultural impacts of climate change.
First, mathematical programming (MP) has
been used to explore how predicted yield losses from climate change would cause American farmers to change crops (Adams et al. 1990) and switch between crops and livestock (Adams et al. 1999).
The MP approach has been successfully implemented in
the US but it is difficult to calibrate and so has not been broadly adopted elsewhere. The MP approach also places all the burden of including adaptation on the analyst.
To
the extent that the analyst is unaware of how farmers may be able to adapt to climate change and alter predicted yield losses, the results can be biased.
A second approach in
the literature is to examine panel data and explore the intertemporal changes in net revenues with weather (Deschenes and Greenstone 2007).
Using fixed effects, this
approach can isolate the impacts of unexpected weather events on farm outcomes.
It is
a reasonable way to measure the impact of weather surprises. However, it is not an accurate portrayal of the impacts of climate changes, which will be visible to farmers as they occur.
With climate change, farmers can make adjustments in what farm type they
choose, what they plant, and how they grow different crops. None of these adaptations are possible with weather surprises.
Further, most of the countries at risk from climate
change (low latitude countries) do not have reliable panel data to estimate the intertemporal approach (with the possible exception of Brazil and India).
This paper builds on the traditional Ricardian approach, where land value or net revenue is regressed on climate and other exogenous factors to reveal the sensitivity of agricultural land to climate (Mendelsohn et al. 1994; 1996; 1999; 2001; Sanghi 1998; Seo et al. 2005;; Kurukulasuriya et al 2006; Kurukulasuriya and Mendelsohn 2006a; Seo and Mendelsohn 2007b). around the world.
These “Ricardian” results have been successfully estimated
However, the Ricardian studies do not provide insight into how
farmers adapt to climate because they are a reduced form “black box” analyses.
In order
to capture the role of irrigation, separate regressions could be estimated for irrigated and dryland samples (Schlenker et al. 2005).
However, this approach treats the choice of
irrigation as exogenous and it suffers from sample selection bias. By explicitly modeling adaptations such as irrigation, this paper builds a more complete model where such
choices by farmers are endogenous.
The paper is divided into five parts. The next section develops a theoretical model of the farm with farmers choosing farm types based on climate and other conditions. Farm type choice is estimated with a multinomial logit. Given the choice of farm type, a second stage estimates conditional net revenue.
The third section describes the survey of over
2000 subsistence and commercial farmers across 7 South American countries and other data sources.
The fourth section discusses the cross sectional results.
The fifth section
presents forecasts of impacts from a set of future climate scenarios.
We compare the
forecasts one would make assuming farm type choices are endogenous with the results if one assumed the choices remained constant.
We conclude the paper with the policy
implications and the limitations of the paper.
II. Theoretical Model
We assume that farmers choose amongst five types of farms: crops only dryland, crops only irrigated, crops and livestock (mixed) dryland, crops and livestock (mixed) irrigated, and livestock only farming.
Given these choices, the farmer combines inputs to make
outputs that maximize land value.
We assume that the farmer will choose the
combination of farm type and irrigation that maximizes expected net revenues.
In Figure 1, we show a hypothetical relationship between farm type and climate. picture suggests that each farm type is ideal for a particular climate range.
The
As climate
changes, farmers switch from one farm type to another. captures this switching.
The overall response function
The model explicitly captures the switching as farmers stay on
the maximum profit locus at different temperatures or precipitation levels.
More formally, each farmer maximizes profit by choosing a farm type j (j=1, …, 5):
π 1 = Xβ1 + u1 π j * = Zγ j + η j ,
(1) j=1,…, J.
(2)
where E (u1 | X , Z ) = 0 and var(u1 | X , Z ) = σ 2 . The subscript j is a categorical variable
indicating the choice amongst J alternatives. The vector Z represents the set of explanatory variables for all the alternatives and the vector X contains the determinants of the variable of interest. Without loss of generality, the profit for alternative 1 is observed only if it is chosen, which happens when
arg max {π
* 1
, π * 2 ,..., π * J } = 1.
(3)
j
Or
π 1* > π k * for ∀ k ≠ 1 [or if η k − η1 < Zγ 1 − Zγ k for k ≠ 1]
(4)
The probability P1 of the first farm type being chosen is
P1 = Pr[η k − η1 < Zγ k − Zγ 1 ] ∀ k ≠ 1
(5)
Assuming η j is independently and identically Gumbel distributed, the probability that farmer will choose farm type 1 among the 5 farm types is (McFadden 1981):
P1 =
exp(Zγ 1 )
∑
.
5
exp(Zγ k ) k =1
(6)
The choice equation is identified using cross price terms.
The parameters are
estimated by Maximum Likelihood Method using an iterative nonlinear optimization technique. These estimates are CAN (Consistent and Asymptotically Normal) under standard regularity conditions (McFadden 1999).
The probability of choice is identified
by both cross price terms for crops and livestock and adding up constraints across the probabilities.
Given the choice of the farm type 1, the farmer will choose inputs and outputs to maximize the net revenue from farming. The maximum profits can be estimated as a function of exogenous variables X directly from equation 1 above. However, it is likely that the errors in equation 1 and equation 2 are correlated. As profits are only observed for the farms that chose farm type 1, selection bias should be corrected to obtain consistent estimates of the parameters (Heckman 1979). Following Dubin and McFadden (1984), we assume the following linearity condition:
J
E(u1 |η1 ,...,ηJ ) = σ ⋅ ∑rj ⋅ (η j − E(η j )) , with j =1
J
∑r
j
j=1
=0
(7)
We can the estimate the conditional profit function for farm type 1 as follows:
5
Pk ⋅ ln Pk + ln P1 + δ 1 1 − Pk
π 1 = X 1ϕ1 + σ ⋅ ∑ rk ⋅ k ≠1
(8)
Note that η in equation 2 and δ in equation 7 are now independent.
The regressors in the above equation include soils, climate, and socio-economic variables such as the provision of electricity and ownership of computer. Country dummy variables are also tested to see if country specific conditions make a substantial difference. We follow the previous studies in specifying the functional form of the equation as a quadratic form.
In this analysis, we employ land value as the measure of net productivity.
With perfect
competition for land, free entry and exit will drive excess profits to zero on the margin (Ricardo 1817).
In this case, land rents will equal net income per hectare.
Land value
will reflect the present value of the net income of each farm: ∞
Vland = ∫ π t* ⋅ e −rt dt
(9)
0
, where r is the market interest rate. (Mendelsohn et al. 1994)
Land values provide a better measure of climate response than annual net revenue because they reflect the expectation of net revenues across many years.
In contrast,
annual net revenues are influenced by year by year variation in weather and prices.
Since we are interested in this analysis in climate not weather impacts, the land value measure is more relevant. However, the land value measure also captures the farmer’s expectations about other things that might change in the future. For example, if farmers expect that technical change will enable them to cultivate the same plot more productively in the future, it will be reflected in land value. Further, future development may increase land prices. The sample used in this analysis does not include farms near metropolitan areas so as to avoid this potential source of bias.
The expected value of the farm, W, is the sum of the probabilities of each farm type times the conditional land value of that farm type.
That is:
5
W (C ) = ∑ Pk (C ) * π k (C )
(10)
k =1
Note that this measure does not assume a farm will remain as one type.
The change in
welfare, ∆W, resulting from a climate change from CA to CB can be measured as follows.
∆W = W (C B ) - W (C A )
(11)
This change in welfare captures both changes in the probability a farm will be a particular type and the conditional value it would have as that type.
III. Data and Background
Farm surveys were pretested and then finalized 4 . Spanish or Portuguese depending on the country.
Each survey was translated to Farm surveys were collected by
country teams from seven countries in South America5. The seven countries include: Argentina, Brazil, Chile, Colombia, Ecuador, Uruguay, and Venezuela.
Random
samples of districts were selected to observe a set of farms over a wide range of climates within each country.
In each country, 15-30 clusters were selected and 20-30
households were interviewed in each cluster. Cluster sampling was done to control the cost of the survey. Both small and large farms were included in the sample. The farm surveys asked questions about farming activities, including crop and livestock production and costs.
The survey was conducted from July 2003 to June 2004. Surveys also
recorded the climate and weather related perceptions of the farmers. Altogether, a total of 2003 farms were surveyed.
Climate data come from two sources: temperature observations came from US Defense Department Satellites and the rainfall observations came from ground station data of the World Meteorological Organization (WMO 1989). The satellite temperature measures proved superior to the weather station observations at least for rural areas of the world (Mendelsohn et al 2007). The satellites can observe the entire surface of the earth whereas many rural areas do not have a weather station nearby and so require interpolation. Unfortunately, the satellites cannot directly measure precipitation and so 2
Survey forms are available from the authors. We wish to thank Flavio Avila for managing the 7 country data collection process. We also wish to thank the team leaders of the collection process in each country: A. Albin, J. Gonzalez, P. Granados, L. Irias, P. Jativa, J. Lozanoff, and R. Pacheco. 5
the weather station data is the best that can be done at the moment.
Soil data were obtained from the FAO digital soil map of the world CD ROM (FAO 2003). The data was extrapolated to the district level using Geographical Information System. The data set reports 116 dominant soil types organized into 26 major groups. We extract texture and slope of the soils at the district level.
The analysis relies upon land values and farm characteristics as reported by the interviewed farmer.
In many parts of Latin America, land has been reallocated by the
government (Mendelsohn 1994). Land use is also restricted in many cases.
For example,
farmers in Brazil face official limitations on land clearing. The analysis was not able to control for all of these imperfections in the land market. However, separate analyses comparing Ricardian regressions that use land values and net revenues for the dependent variable lead to very similar results, suggesting the land value data is consistent and unbiased (Seo and Mendelsohn 2007).
IV. Empirical Results
The study identified five types of farms in the region: crop-only dryland, crop-only irrigated, mixed (crops and livestock) dryland, mixed irrigated, and livestock-only farms. Table 1 measures how many farms of each type were in the sample.
Over half of the
farms have both livestock and crops, almost one third of the farms rely solely on crops, and only 13% of the farms just raise animals. Three fourths of the farms growing crops
use dryland farming and the rest use irrigation.
Our first analysis seeks to explore how different exogenous factors and specifically climate affect the choice of farm type. We conduct a multinomial logit omitting the choice of livestock-only farms for comparison. The results are displayed in Table 2. With the livestock only farms as the base case, four regressions are presented for each farm type. The climate coefficients are mostly very significant. For the four regressions, the response to higher temperatures is hill shaped whereas the response to higher precipitations is U shaped. Soils Acrisols, Cambisols, Gleysols decrease the probability of each farm types chosen relative to livestock-only farms. Soil Planosols, however, increase the probability of crop only dryland and mixed dryland farming to be chosen. Commercials farms do not show any substantially different responses. Crop-only irrigated farms are more often chosen when tomato prices are high. When maize prices are high, each of the four farm types are chosen more often compared to livestock-only. However, when potato prices are high, livestock-only is chosen more often.
Table 3 describes the marginal effects of temperature and precipitation on farm type choice. Higher temperatures encourage farmers to move away from crop-only farms to mixed farms and livestock farms. Higher precipitation pushes farmers to adopt dryland farming and avoid expensive irrigation investments.
The second stage estimates of conditional land values for each farm type are displayed in Table 4. Climate coefficients are generally significant for all the farm types and differ in their magnitude and signs by farm types. For example, summer temperature is
significant only for dryland crop-only farms and livestock-only farms. Crop-only farms show a hill shape response to summer temperature whereas livestock farms show a U shaped response. Summer precipitation is highly significant for all types of farms. Farm land values show a hill shaped response to summer precipitation except for the mixed irrigated farms. Soils play an important role in determining land value. Gleysols reduce farm land values, but Luvisols and Planosols increase values at least for crop-only dryland farms. Planosols increase the land value of crop-only dryland farms, but decreases the value of mixed dryland farms. Clay soils generally reduce agricultural land values with the exception of crop only irrigated farms.
Table 4 also displays selection bias terms for each farm type. Many of the selection terms are significant which suggest that selection bias is an important issue. When the selection model suggests a farm should be mixed irrigated, that farm has a lower value if it is actually crop-only dryland or livestock-only.
When the selection model predicts a
farm is livestock-only, that farm has a lower value if it is actually crop-only irrigated or mixed dryland.
On the other hand, if the selection model predicts a farm will be crop-
only, it has a higher value if it is actually a mixed farm.
Table 5 presents the marginal effects and elasticities of annual temperature and precipitation evaluated at the mean of the sample.
Warming decreases the value of crop-
only farms and especially livestock-only farms but it increases the value of mixed farms. Increased precipitation raises the value of all farms but especially livestock-only and crop-only farms.
V. Climate Change Impacts Simulations
In this section, we use the cross sectional results to predict the impact of future climate scenarios. There are several caveats one must keep in mind with such forecasts.
First,
we assume that comparing a cool farm to a warm farm today is the same as having a farm experience a cool climate today versus a warm climate in the future.
If there are
important missing variables in our analysis that are correlated with climate, the predictions will be biased. Second, we assume that other changes in future conditions will not affect our climate predictions. For example, changes in technological advances, growth, and land use will not alter climate impacts.
In practice, these future changes are
both likely to occur and likely to have an effect on climate impacts. Future analyses should take these changes into account, but this is beyond the scope of this paper. This simulation just examines the impact of future climate change on the current agricultural system. Third, we assume that prices will not change in any of these future scenarios even if supply changes dramatically. Partially, this can be justified because prices are determined in a world market and regional changes are not a good predictor of global changes. However, if prices rise as a result of output falling, this will tend to reduce the welfare impacts predicted in this analysis (Mendelsohn and Nordhaus 1996). Fourth, the analysis does not consider carbon fertilization effects. The increase in carbon dioxide is expected to be beneficial to plants in general and to specific crops in particular. Carbon fertilization is not taken into account in these forecasts although it will clearly increase productivity.
In order to see what impact future climates might have on South American agriculture, we examine three climate scenarios generated by Atmospheric Oceanic General Circulation Models (AOGCM’s). The three models we rely upon provide a broad array of outcomes from a mild wet scenario to a very hot and dry scenario. Specifically, the three models are the Parallel Climate Model (PCM) (Washington et al. 2000), the Center for Climate System Research (CCSR) (Emori et al. 1999), and the Canadian Climate Centre (CCC) (Boer et al. 2000). The climate projections of these three models for South America are presented in Table 6. The PCM is the mildest scenario with small amounts of warming, small increases in summer precipitation, and large increases in winter precipitation. The CCC is the harshest scenario with substantial warming and reductions in summer precipitation. The CCSR scenario lies between these other scenarios. Temperature increases steadily over this century across all three models. Precipitation increases and decreases over time in no apparent pattern.
For each climate scenario, we make two predictions.
In one prediction, we assume that
the decision to choose farm type and irrigation is exogenous and will not change.
In the
second prediction, we assume these choices are endogenous and will change with each climate scenario. That is, we predict how each climate scenario will change the probability each farmer will choose each farm type (using the coefficients in Tables 2). Combining these results with the changes in the conditional land values (using the coefficients in Table 4) yields an expected change in the land value for each farm for both the exogenous and endogenous cases.
Table 7 demonstrates what happens to farm choices in the three climate scenarios over
time. There will be fewer crop-only farms under the hot and dry CCC and CCSR scenarios. With the mild and wet PCM scenario, however, there will be more crop-only dryland farms but less crop-only irrigated farms.
The reductions increase over time and
the increases diminish over time. The effects on mixed farms will be small except for the 2100 effects. The CCC scenario predicts there will be fewer mixed farms whereas the PCM scenario predicts more mixed farms. The largest changes occur in the frequency of livestock-only farms. They will increase dramatically in the CCC and CCSR scenarios and fall in the PCM scenario. Effects will intensify over time as the climate change signal intensifies.
Table 8 describes the changes in conditional income in each climate scenario over time. The results are consistent with the choice decisions. As the warming signal intensifies over time, farmers earn significantly less profits from growing crops whereas the profits from managing both crops and livestock are relatively more resilient. With the CCC scenario, the income from growing crops alone falls steadily as temperature warm. With the CCSR scenario, crops initially do better but then fall as the warming signal intensifies. Only with the mild and wet PCM scenario do South American dryland crops do substantially better with climate change. With the PCM scenario, the income from irrigated crops falls as the increased precipitation makes the cost of installing irrigation superfluous.
If temperatures increase slightly, livestock farmers earn slightly more
money but as temperatures increase further, revenues from livestock management fall. However, income falls less for livestock management than for crops making livestock management relatively more attractive at higher temperatures. With the PCM scenarios, incomes from livestock management increase, but much less than crop incomes increase.
Consequently, farmers are more likely to choose livestock in the CCC and CCSR scenarios but less likely in the PCM scenario.
From the first stage choice decisions and the second stage conditional income decisions, we can estimate expected land values. Table 9 reports the change in expected land value under each climate scenario. Despite the adaptations that farmers make to climate changes in each scenario, warming still has powerful impacts on expected land value of South American farms. Farm values fall under the CCC and CCSR scenarios, but increase in the PCM scenario. The size of the harmful effects increases over time. For example, the expected damages by 2020 in the CCC and CCSR scenarios are between 5% to 10% by 2020, but almost 20% by 2060. The PCM scenario, in contrast, predicts a gain of about 45% in every period.
The consequence of climate change in South
America clearly depends on the climate scenario.
VI. Conclusion This study blends two strands of economic literature to develop a more in depth understanding of the effect of climate change on agriculture. One strand is the adoption literature that explains what factors influence farmer’s decisions to adopt different technologies. The other strand is the Ricardian literature that has developed an effective method of measuring climate change impacts across the world. By marrying the two approaches, this new study reveals the precise adaptations that farmers are making to climate change. change.
The method, however, also measures the economic impact of climate
By revealing more of the details of what is actually happening on farms, this
new approach moves from the black box nature of the original Ricardian model towards the detailed portrayal of farms in the agronomic-economic literature.
The paper models the choice of whether to grow crops, own livestock, and install irrigation and tests whether these choices are influenced by temperature and precipitation. Although such variables were sometimes included in the technology adoption literature, the literature did not examine the effect of climate change on adaptation. Using cross sectional evidence, the paper models how Latin American farmers have adapted to the range of climates across the continent. Surveys of over 2000 farmers provide detailed information about crops, livestock and irrigation choices. Relying on a two stage integrated model of a farm, the choice of farm type and irrigation, and conditional land values were all calculated.
The results show that the choice of farm type and irrigation are very sensitive to climate. Farmers are more likely to pick crops-only in cooler temperatures and they will choose crop-livestock and especially livestock-only in hotter locations. Farmers will tend to irrigate in locations that are relatively hotter and dryer. Of course, irrigation also requires access to water sources.
Conditional land values are also dependent on climate. A small increase in temperature decreases all farm land values but especially crop-only farms. A small increase in precipitation is deemed beneficial for all types of farms but especially for crop-only dryland farms. The income results thus support the farm choice results. As temperatures warm, (although all farms lose income) farmers move to mixed farms and especially
livestock farms because they earn relatively more income. As precipitation increases (decreases) farmers move towards (away from) crop-only dryland farms because they earn relatively more income.
Applying these cross sectional results to future climate scenarios reveals some interesting outcomes.
If the future climate scenario is very hot and dry, expected land
values will fall over time. This loss can be attributed to the large damage to crops-only non-irrigated farms. Under this scenario, dryland crop-only farming will be especially hard hit. Crop-livestock operations will be hurt but less so. Although livestock-only farms will increase, the income from such operations will not be enough to compensate for the losses from crops.
If the future scenario is mild and wet, crop-only irrigated
farming will decline in value. However, the increased value of crop-only dryland farming will more than compensate and overall land values will increase. The impacts of climate change consequently depend a great deal on the climate scenario.
The overall results suggest that farmers will do a great deal of adaptation in response to climate change.
The results indicate that they will change whether they grow and own
livestock, whether they will grow crops, and whether or not they will rely on irrigation. Nonetheless, they will not be immune form the effects of climate change.
South
American farms will remain vulnerable to hot and dry scenarios.
There are a number of caveats that must be kept in mind in interpreting these results. First, there was no information about off-farm water resources in the analysis and so this important variable was omitted. Second, the effect of carbon fertilization was not
captured in the analysis since all the farms in the sample were exposed to the same level of carbon dioxide. Carbon fertilization is likely to improve future crop productivity and thus may offset some of the harmful effects predicted in this analysis. Third, the influence of technical change is not captured in this study. Future productivity increases may offset some of the losses predicted in this analysis and technological advances in breeding could create future crops and animals that are more heat tolerant. Such possible effects are not considered. Fourth, the paper assumes that commodity and labor prices would not change with climate. If prices increase as a result of reductions in output, the welfare damages will be smaller. Finally, the analysis assumes that farmers in the future will be able to adapt as readily as farmers in the present. That is, the study assumes that the adaptations one currently sees from place to place can be done across time as climate change unfolds. All of these factors should be considered when projecting the future outcomes of climate change.
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Table 1: Number of Farms of Each Type
Dryland
Irrigated
All
Crop-Only
360
277
637
Crop and Livestock
948
179
1127
Livestock-Only
268
1
269
All
1576
457
2003
Table 2: Multinomial Logit Model of Farm Type Selection
Crop-Only Dryland Intercept
Crop-Only Irrigated
-7.67
10.71
-2.211
1.07
0.914
14.03
0.568
6.35
-0.036
30.06
-0.025
16.35
-0.014
7.01
-0.029
28.61
0.0000
6.03
0.0001
16.79
1.027
88.25
0.572
28.23
Winter Temperature
-0.024
50.19
-0.0119
12.64
Winter Precipitation
-0.023
16.29
-0.026
19.76
Winter Precipitation2
0.0001
17.82
0.0001
9.32
Soil Acrisols
-0.0218
16.38
-0.0438
20.68
Soil Cambisols
-0.0267
7.89
-0.0605
21.89
Soil Gleysols
-0.0239
7.91
-0.0551
14.71
Soil Andosols
0.0030
0.06
-0.0412
6.50
Soil Planosols
0.0077
4.41
-0.0063
2.24
Soil Xerosols
0.0042
0.15
-0.0172
2.29
Maize Price
1.154
28.04
1.029
22.36
Potato Price
-17.269
10.18
-3.463
0.41
Tomato Price
-3.293
10.06
0.541
0.83
0.005
0.00
0.189
3.13
Summer Temperature 2
Summer Temperature
Summer Precipitation 2
Summer Precipitation Winter Temperature 2
Commercial Dummy
Notes: 1) Omitted choice is livestock only. 2) Three test statistics and P values for the
model significance are Likelihood Ratio test: P
