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How China's farmers adapt to climate change.


This paper examines how farmers have adapted to the current range of climates across China. A cross sectional sec·tion·al  
1. Of, relating to, or characteristic of a particular district.

2. Composed of or divided into component sections.

 method is used to analyze irrigation irrigation, in agriculture, artificial watering of the land. Although used chiefly in regions with annual rainfall of less than 20 in. (51 cm), it is also used in wetter areas to grow certain crops, e.g., rice.  choice and crop choice across 8,405 farmers in 28 provinces in China. A discrete choice In economics, discrete choice problems involve choices between two or more discrete alternatives, such as entering or not entering the labor market, or choosing between modes of transport.  logit The logit function is an important part of logistic regression: for more information, please see that article.

In mathematics, especially as applied in statistics, the logit
 model is used to capture the choice of irrigation and a multinomial logit model is used to capture crop choice. We find that both irrigation and crop choice decisions are climate sensitive. Chinese Chinese, subfamily of the Sino-Tibetan family of languages (see Sino-Tibetan languages), which is also sometimes grouped with the Tai, or Thai, languages in a Sinitic subfamily of the Sino-Tibetan language stock.  farmers are more likely to irrigate ir·ri·gate
To wash out a cavity or wound with a fluid.
 when facing lower temperatures and less precipitation. Farmers in warmer places are more likely to choose oil crops, maize maize: see corn. , and especially cotton and wheat, and are less likely to choose vegetables, potatoes, sugar and especially rice and soybeans. In wetter locations, farmers are more likely to choose soybeans, oil crops, sugar, vegetables, cotton and especially rice, and they are less likely to choose potato, wheat and especially maize.

The analysis of how Chinese farmers have adapted to current climate, provides insight into how they will likely adapt when climate changes. Future climate scenarios will cause farmers in China to want to reduce irrigation and shift crops towards oil crops, wheat, and especially cotton. In turn, farmers will shift away from potatoes, rice, vegetables, and soybeans. We find, however, that adaptation will likely vary from region to region. For example, irrigation is likely to fall in the eastern regions of China but increase in the western regions (provided there is sufficient water). One important weakness of this study is that it was not able to take into account water availability, which is a critical element of Chinese agriculture. The projections into the future take into account only climate change. The analysis does not take into account other background changes that may well occur including changes in prices, technology, and water availability.

Analyses of climate impacts must take adaptation into account or they will overestimate o·ver·es·ti·mate  
tr.v. o·ver·es·ti·mat·ed, o·ver·es·ti·mat·ing, o·ver·es·ti·mates
1. To estimate too highly.

2. To esteem too greatly.
 damages. Policy makers must be aware that adaptation is an endogenous endogenous /en·dog·e·nous/ (en-doj´e-nus) produced within or caused by factors within the organism.

1. Originating or produced within an organism, tissue, or cell.
 response to climate. They should anticipate that adaptation is important, that the magnitude of changes depends on the climate scenario, and that the desired changes depend on the location of each farm.


Although there is an extensive literature on the effects of climate on agriculture (Reilly et al 1996; McCarthy Mc·Car·thy   , Joseph Raymond 1908-1957.

American politician. A U.S. senator from Wisconsin (1947-1957), he presided over the permanent subcommittee on investigations and held public hearings in which he accused army officials, members of the media,
 et al 2001), there are very few studies that have measured adaptation. Studies that compare the impacts of climate change that include adaptation, such as Ricardian Ricardian
a believer in the economic theories of David Ricardo, English economist, especially that rental income is an economic surplus. — Ricardian, adj.
See also: Economics
 studies (Mendelsohn, Nordhaus, and Shaw 1994, Mendelsohn and Dinar 1999; Mendelsohn et al. 2001; Mendelsohn and Dinar 2003; Seo et al. 2005; Kurukulasuriya et al. 2006; Fleischer Fleischer (or Fleisher) is a common German family name. Its literal meaning is "butcher". Other German family names with the same meaning include Metzger, Mezger, Flesher, Fleischman, and Fleischmann.  et al 2007; Seo and Mendelsohn 2007, Wang (Wang Laboratories, Inc., Lowell, MA) A computer services and network integration company. Wang was one of the major early contributors to the computing industry from its founder's invention that made core memory possible, to leadership in desktop calculators and word processors.  et al. 2008), tend to find much lower damages than studies that do not include adaptation, such as agronomic a·gron·o·my  
Application of the various soil and plant sciences to soil management and crop production; scientific agriculture.

 analyses (Rosenzweig Rosenzweig, or Rosensweig (meaning "rose twig") can refer to: People
  • Ádolf Agai (originally Rosenzweig), a Hungarian physician and journalist[1]
  • Adolf Rosenzweig, a Hungarian-German rabbi[2]
 and Parry 2004; Reilly et al. 1996; McCarthy et al 2001; Parry et al. 2004). It is clear from this empirical evidence that it is very important to include adaptation in any impact analysis of long term climate change. Of course, adaptation is likely to be less important with respect to year to year weather fluctuations as farmers may have fewer options to adapt to sudden or abrupt changes.

Adaptations are actions that people and firms take in response to climate change to reduce damages or increase benefits (IPCC See IMS Forum.  2007). (1) What specifically do farmers do to adapt to climate? How have they adjusted to the climates that they live in today? A new series of studies have begun to examine this question. By comparing what farmers do in one climate zone versus another, the studies quantify Quantify - A performance analysis tool from Pure Software.  how farmers have made long term adjustments to climate. For example, studies have examined how climate affects the choice of irrigation in Africa (Kurukulasuriya and Mendelsohn 2006a) and South America South America, fourth largest continent (1991 est. pop. 299,150,000), c.6,880,000 sq mi (17,819,000 sq km), the southern of the two continents of the Western Hemisphere.  (Mendelsohn and Seo 2007). Studies have explored how climate affects livestock livestock

Farm animals, with the exception of poultry. In Western countries the category encompasses primarily cattle, sheep, pigs, goats, horses, donkeys, and mules; other animals (e.g., buffalo, oxen, or camels) may predominate in other areas.
 choice in Africa (Seo and Mendelsohn 2006) and South America (Seo and Mendelsohn 2007a). Previous studies have explored how climate alters crop choice in Africa (Kurukulasuriya and Mendelsohn 2006) and South America (Seo and Mendelsohn 2007b). All of the above mentioned adaptation studies find that farmers adjust irrigation practices, crop varieties, and livestock species to both temperature and precipitation levels.

In the present analysis, we use the same cross sectional methods used in the above studies of Africa and South America to study farm adaptation in China. We expect that farmers in China have also adapted to the range of climates that they face in China. Analyzing a sample of 8,405 farms sampled across 28 provinces in one year, we estimate logit models of irrigation and multinomial logit regressions of crop choice to detect how these choices vary with long term temperature and precipitation. Matching the location of each farm to climate data and soils, it is possible to examine the effect of climate on these endogenous choices by farmers while controlling for several other factors.

The available data allow us to measure the direct effects of temperature and precipitation on irrigation choice and crop choice. We specifically examine the choice of 9 major crops in China: wheat, rice, maize, soybean soybean, soya bean, or soy pea, leguminous plant (Glycine max, G. soja, or Soja max) of the family Leguminosae (pulse family), native to tropical and warm temperate regions of Asia, where it has been , potato, cotton, oil crops, sugar, and vegetables. Unfortunately, the amount of irrigation water a farmer uses is not available in the dataset See data set. . We do not know water availability or cost. If future climate scenarios reduce available water supplies, this is likely to affect these choices and the present study does not take this into account. This is an important omission omission n. 1) failure to perform an act agreed to, where there is a duty to an individual or the public to act (including omitting to take care) or is required by law. Such an omission may give rise to a lawsuit in the same way as a negligent or improper act.  for an agricultural system such as in China that relies heavily on irrigation.

The paper is organized as follows. We briefly review the methodology of adaptation analysis in the next section. Section three discusses the available data and the construction of the variables in the data set. In the fourth section, we present the estimation estimation

In mathematics, use of a function or formula to derive a solution or make a prediction. Unlike approximation, it has precise connotations. In statistics, for example, it connotes the careful selection and testing of a function called an estimator.
 results for current farmers. The fifth section then forecasts how future farmers would change their irrigation and crop choice for three different climate scenarios in 2050 and 2100. The paper concludes with a summary of the key results and a discussion of policy relevance.


We assume that farmers make choices that maximize their income. We define income broadly to include both products they sell and consume. In this analysis, we are interested in modeling how they select from a number of discrete and mutually exclusive Adj. 1. mutually exclusive - unable to be both true at the same time

incompatible - not compatible; "incompatible personalities"; "incompatible colors"
 choices (McFadden McFadden may refer to:
  • Bernarr Macfadden
  • Bob McFadden
  • Brian McFadden (b. 1980), Irish singer
  • Bryant McFadden (b. 1981), NFL
  • Cynthia McFadden, correspondent for the American Broadcasting Company
  • Cyra McFadden, American writer
  • Daniel McFadden (b.
 1981). In order to study irrigation choice, we rely on a dichotomous di·chot·o·mous  
1. Divided or dividing into two parts or classifications.

2. Characterized by dichotomy.

 logit. We test how climate influences the probability of choosing whether to irrigate or not, while controlling for a number of other independent variables such as soils, household characteristics and farm characteristics.

In order to study crop choice, we rely on a multinomial logit regression regression, in psychology: see defense mechanism.

In statistics, a process for determining a line or curve that best represents the general trend of a data set.
. The multinomial logit examines the probability that a farmer chooses one of the 9 major crops grown in China. We do not examine minor crops. We assume that the choice among the 9 crops is independent of these other choices. The multinomial tests the influence of climate on the probability of choosing each crop controlling for a number of other independent variables such as soils, household characteristics, and farm characteristics. We model irrigation choice and crop choice separately.

We assume that farmers choose the crop that yields the highest net profit. Hence, the probability that a crop is chosen depends on the profitability of that crop. We assume that farmer i's profit in choosing crop j (j=1, 2, ... , J) is:

[[pi].sub.ij] = [V.sub.j]([C.sub.i], [K.sub.i], [S.sub.i]) + [[epsilon].sub.j]([C.sub.i], [K.sub.i], [S.sub.i]) (1)

where C is a vector of climate variables, K is a vector of exogenous Exogenous

Describes facts outside the control of the firm. Converse of endogenous.
 characteristics of the farm, and S is a vector of characteristics of the farmer. The vector K includes soils, elevation elevation, vertical distance from a datum plane, usually mean sea level to a point above the earth. Often used synonymously with altitude, elevation is the height on the earth's surface and altitude, the height in space above the surface.  and access variables; S includes variables such as the education of the farmer and land size. The profit function is composed of two components: the observable component V and an unobservable component that is in the error term s. The farmer will choose the crop that yields the highest profit. Similarly, the farmer also chooses irrigation or rainfed farming based on which type of farming yields the highest income.

The farmer will choose crop j over all other crops k (j [not equal to] k) if:

[[pi].sup.*.sub.j]([Z.sub.i]) > [[pi].sup.*.sub.k] ([Z.sub.i]) for [for all] k [not equal to] j. [or if [[epsilon].sub.k]([Z.sub.i]) - [[epsilon].sub.j]([Z.sub.i]) < [V.sub.j]([Z.sub.i]) - [V.sub.k]([Z.sub.i]) for k [not equal to] j]. (2)

The probability [P.sub.ij] for the jth crop to be chosen is then:

[P.sub.ij] = Pr[[[epsilon].sub.k]([Z.sub.i]) - [[epsilon].sub.j]([Z.sub.i]) < [V.sub.j] - [V.sub.k]] [for all] k [not equal to] j where [V.sub.j] = [V.sub.j]([Z.sub.i]) (3)

We are interested in the following specific model where climate has a quadratic quadratic, mathematical expression of the second degree in one or more unknowns (see polynomial). The general quadratic in one unknown has the form ax2+bx+c, where a, b, and c are constants and x is the variable.  functional form:

[V.sub.j]([C.sub.i], [K.sub.i], [S.sub.i]) = [C.sub.i][alpha] + [C.sup.2.sub.i] [beta] + [K.sub.i] [empty set] + [S.sub.i] [omega] (4)

Assuming that [epsilon] is independently Gumbel Gumbel may refer to:
  • Gumbel distribution, named after E. J. Gumbel
  • Bryant Charles Gumbel (1948–), American television sportscaster, brother of Greg
  • Emil Julius Gumbel, (1891–1966), German mathematician, pacifist and anti-Nazi campaigner
 distributed and the profit function can be written linearly in its parameters, the probability can be calculated as follows:

v. re·pro·duced, re·pro·duc·ing, re·pro·duc·es
1. To produce a counterpart, image, or copy of.

2. Biology To generate (offspring) by sexual or asexual means.
 IN ASCII ASCII or American Standard Code for Information Interchange, a set of codes used to represent letters, numbers, a few symbols, and control characters. Originally designed for teletype operations, it has found wide application in computers. ] (5)

which gives the probability that farmer i will choose crop j from among J species (McFadden 1981).

The marginal change in probability of selecting a crop with respect to climate is therefore:

[partial derivative derivative: see calculus.

In mathematics, a fundamental concept of differential calculus representing the instantaneous rate of change of a function.
][P.sub.j]/[partial derivative][c.sub.l] = [P.sub.j][[[alpha].sub.jl] + 2[][[beta].sub.jl]] - [J.summation summation n. the final argument of an attorney at the close of a trial in which he/she attempts to convince the judge and/or jury of the virtues of the client's case. (See: closing argument)  over (k=1)] [P.sub.k][[[alpha].sub.kl] + 2[][[beta].sub.kl]]. (6)

The marginal probability of choosing a new crop depends on the underlying climate of the farm.

A critical assumption of the multinomial logit is Independence of Irrelevant Alternatives Independence of irrelevant alternatives (IIA) is a term for an axiom of decision theory and various social sciences. Although exact formulations of IIA differ, intentions of the usages are similar in attempting to provide a rational account of individual behavior or aggregation of . We assume that the relative probability of any two alternatives is not affected by adding a third choice.

The same discrete choice model applies to irrigation except in this case there are only two choices. In the irrigation analysis, we rely on a logit regression to estimate the probability of irrigation. The logit model predicts that the probability of irrigation can vary between 0 and 1 depending on the values of the independent variables. We again test the influence of climate controlling for a number of other independent variables such as soils, household characteristics, and farm characteristics.


The climate data (monthly temperature and precipitation) were obtained from the National Meteorological me·te·or·ol·o·gy  
The science that deals with the phenomena of the atmosphere, especially weather and weather conditions.

[French météorologie, from Greek
 Information Center in China. The data are based on actual measurements in 753 national meteorological stations that are located throughout China. The temperature and precipitation data were collected from 1951 to 2001. We rely on the mean values of these variables (climate normal) over this time period for each month. We average the monthly climate data into four seasons in the following way: winter is the average of December December: see month.  to February February: see month. , spring is the average of March to May, summer is the average of June to August, and fall is the average of September September: see month.  to November November: see month. .

Socio-economic socio-economic adjsocioeconómico

socio-economic adjsocioéconomique 
 data is obtained from the Household Income and Expenditure Survey (HIES HIES Hyper-IgE Syndrome ) administered by China National Bureau of Statistics in 2001. There are more than 50,000 observations in the HIES. We have selected a sub-sample from only those counties for which we have climate data (from meteorological stations located in these counties). Our final sample has 8,405 households in 915 villages in 124 counties in 28 provinces.

The HIES includes a number of household and village characteristics. Irrigation data were collected at the village level. Information about crop choice was collected at the farm level. Nine major crops were identified: cotton, maize, oil crops, potato, rice, soybean, sugar, wheat and vegetables. Additional household variables include the education level of members of the farm household, each family's land area, the number of family laborers that belong to the household. Additional village variables include indicators about the topographical environment of each village (e.g., if it is located on a plain or in a mountainous moun·tain·ous  
1. Having many mountains.

2. Resembling a mountain in size; huge: mountainous waves.


 region), the share of cultivated cultivated,
n in herbal medicine, used to describe plants that are commercially farmed rather than collected from the wild.
 area that is irrigated in the village, membership in associations, extension service, and access to markets (e.g., the presence of paved pave  
tr.v. paved, pav·ing, paves
1. To cover with a pavement.

2. To cover uniformly, as if with pavement.

3. To be or compose the pavement of.
 roads between the village and key services; the distance to each township's government).

To account for soils, we downloaded a soil map from FAO's website. There are three major soil types--clay, sand and loam soils. The final set of soil variables for our analysis was created by generating a variable measuring the share of cultivated area with each type of soil.


In this section, we report the results concerning irrigation choice and crop choice across farmers operating under present situation. This is a cross sectional analysis exploring how the range of current climate across China affects choices of irrigation and crops today.

Irrigation choice

We estimate two logit regressions of irrigation using a quadratic model of climate (Table 1). Because the household characteristics could be considered endogenous, they are included in one model and excluded from the second model. The regression results confirm that climate has a significant impact on a farmer's irrigation adoption decision. Both temperature and precipitation variables are significant. The quadratic climate terms are significant suggesting the climate relationship is nonlinear A system in which the output is not a uniform relationship to the input.

nonlinear - (Scientific computation) A property of a system whose output is not proportional to its input.
 as expected. The seasonal coefficients are not alike suggesting that the consequences of warming and precipitation are not the same in each season. Several of the control variables are significant. Irrigation is more likely with clay and silt soils, when the farm is on a flat plain, and near a road. Farms further away from township township: see town.  governments are less likely to irrigate possibly because the farm is more remote and possibly because the farm is less likely to receive extension services. Household characteristics such as participating in production associations and the level of education of the labor increase the likelihood of irrigation. More land per household member reduces the likelihood of irrigation, probably because irrigated land is more labor intensive Labor Intensive

A process or industry that requires large amounts of human effort to produce goods.

A good example is the hospitality industry (hotels, restaurants, etc), they are considered to be very people-oriented.
See also: Capital Intensive, Trading Dollars
 relative to rainfed land.

In order to interpret the coefficients of the logit model, we calculate marginal impacts for each climate variable (Table 2). The results suggest that seasons have strong but offsetting effects. Warmer summer and fall temperatures increase the likelihood of irrigation selection but warmer spring and winter temperatures reduce it. Wetter springs, summers and falls reduce the likelihood of irrigation selection but wetter winters increase it. In general, warmer annual temperatures reduce irrigation selection likelihood. It implies that farmers in warmer locations are much less likely to choose irrigation and farmers in cooler places are more likely to irrigate. In addition, farmers in locations with more rainfall are also less likely to irrigate because farmers get sufficient moisture without the expense of irrigation. However, the marginal effects depend on the distribution of seasonal rainfall and temperature so that it can vary from place to place.

The analysis does not include water availability. This remains an important caveat to the results. If there is not enough water, farmers may want to shift to irrigation but they may not be able to. Higher temperatures could lead to a reduction in irrigation if higher evaporation evaporation, change of a liquid into vapor at any temperature below its boiling point. For example, water, when placed in a shallow open container exposed to air, gradually disappears, evaporating at a rate that depends on the amount of surface exposed, the humidity  reduces available water. Changes in precipitation could also change flows. As China uses all of its available water in some regions, changes in flows are likely to have very important consequences for irrigation.

Results of crop choice

The analysis of crop choice indicates that farmers plant different crops depending on the climate they face (Table 3). Both temperature and precipitation play a role in crop choice. The quadratic climate coefficients are significant, implying that the response function is nonlinear. The climate coefficients are quite different across seasons suggesting that seasonal effects are, once again, important.

Many of the control variables are significant (Table 3). Soils, as expected, influence crop choice. Cotton and sugar are more likely to be planted on clay soils whereas rice, wheat, vegetables, soybeans and oil crops are less likely. Farmers with silt soils are more likely to choose potatoes but less likely to choose rice, sugar, and several other crops. Cotton and sugar are much more likely to be grown on plains but potato and oil crops are not. Being close to a road increases the likelihood that a farmer will select wheat, rice, vegetables and oil crops and reduces the chance the farmer will select cotton. This may reflect the relative cost of transporting each of these products. The farther the farmer is from the township government, the more likely the farmer will grow wheat and the less likely he will grow oil crops. Proximity to township government makes public extension more accessible. Access to extension may help farmers grow wheat whereas oil crops are relatively simple to grow and so do not require extension services. If a farmer is in a village with major irrigated areas, the farmer is more likely to grow wheat, rice, and sugar but less likely to grow potatoes. Rice and sugar tend to be irrigated whereas potatoes are never irrigated. Farmers who join production associations are more likely to grow cotton, because of the additional ginning gin 1  
A strong colorless alcoholic beverage made by distilling or redistilling rye or other grain spirits and adding juniper berries or aromatics such as anise, caraway seeds, or angelica root as flavoring.
 and marketing needed for the final product, that are taken care by the association. Farms with less educated workers are more likely to grow soybeans and oil crops, which are the least sophisticated crops to grow. The more cultivated land per household member, the more likely the farmer will grow cotton, oil crops, sugar, and wheat--crops that take advantage of economies of scale--but the less likely they will grow rice and vegetables--crops that can be grown on small fields.

In Table 4, we calculate the marginal effects of temperature and precipitation on crop choice. Across China, farmers are more likely to select cotton, wheat, oil crops and maize as temperatures warm and they are less likely to select rice, vegetables, soybeans, potatoes and sugar. With more precipitation, farmers are more likely to pick rice, cotton, vegetables, soybean, oil crops and sugar and less likely to pick maize, wheat and potato. However, the seasonal effects are quite different from the annual effects. For example, warmer summers favor cotton, wheat, and oil crops whereas warmer falls favor rice, vegetables and potato. Wetter springs are very bad for vegetables, oil crops and maize but good for wheat, potato, cotton, and rice. Wetter winters are good for most of the crops (cotton, oil crops, vegetables, soybeans, maize, sugar and rice) but bad for wheat and potato. The actual impact in each location is therefore going to depend on the seasonal distribution of temperature and precipitation, and not just the annual average.


The empirical results in the previous section describe how farmers in China have adapted to the climate that they currently face. They have altered their irrigation and crop choice to fit the climate they live in. In this section, we project how these decisions would change in the future if climate changes. We assume that all other features of each farm will remain the same and that only climate will change. This is obviously a simplification as the technology, capital intensity, and other features of the farm are all likely to change over the next century. However, the analysis does provide a sense of the role of climate and the extent of endogenous adaptation to expect.

We examine a set of climate scenarios from climate models for 2050 and 2100. These climate scenarios are complicated as they predict very different changes in each season, they vary from one region to another within China, and they involve a combination of temperature and precipitation effects. The climate scenarios come from the PCM (1) See phase change memory.

(2) (Plug Compatible Manufacturer) An organization that makes a computer or electronic device that is compatible with an existing machine.
, HADIII, and CCM CCM Contemporary Christian Music
CCM Critical Care Medicine
CCM County College of Morris (New Jersey)
CCM Chama Cha Mapinduzi (political party, Tanzania)
CCM CORBA Component Model
2 climate models. All the models are assuming a business as usual (no abatement) A2 emission scenario. Table 5 presents the average change in temperature and precipitation for China according to according to
1. As stated or indicated by; on the authority of: according to historians.

2. In keeping with: according to instructions.

 each climate scenario. The PCM scenario predicts a small change in temperature and a small increase in precipitation. The HADCM3 scenario predicts a middle temperature increase and a precipitation decline in 2050 but not in 2100. The CCM2 scenario predicts the greatest warming and no change in precipitation in 2050 but a small decline in precipitation in 2100.

Table 5 also presents the effect of each climate scenario on the choice of irrigation and crop choice in 2050 and 2100. All three climate scenarios predict that the likelihood of choosing irrigation will fall in China by 2050. The reductions will range from -13% to -23%, depending on the future climate scenario. By 2100, the percentage of land in irrigation will fall by -1% to -17% depending on the future climate scenario. In addition, all three scenarios also suggest that the likelihood wheat and cotton are selected will slightly increase and that the likelihood of rice and soybeans will slightly decrease by 2050. By 2100, the size of these changes is distinctly larger. Depending on the climate scenario, the likelihood of selecting rice will fall by -1% to -5%, the likelihood of selecting vegetables will fall by -1% to -4%; the likelihood of selecting soybeans will fall by -3% to -4%, and the likelihood of selecting potatoes will fall by -1% to -3%. In turn, the likelihood of selecting wheat will increase by +2% to +3%, the likelihood of selecting oil crops will increase by +1% to +3%, and the likelihood of choosing cotton will increase by +5% to +12%, depending on the climate scenario. These reductions and increases reflect how the percentage of cropland will change. They imply relatively large changes in the mix of crops that will be grown in China.

But the all-China results do not tell the entire story. Table 6 reveals that current irrigation and crop choices vary across regions in China. In order to see this spatial variation, we divide China into 5 major climatic regions: Northeast, Southeast, Middle, Northwest and Southwest. (2) Results show that farmers in the Southeast region are more likely to choose irrigation compared to the other regions. Southeast farmers currently irrigate 68% of their cropland. The share of irrigated land in the Northwest is also very high (60%). In contrast, farmers in the Northeast region use irrigation on only 25% of their cropland.

Farmers in different regions also choose different cropping patterns. For example, in the Northeast region, farmers currently allocate To reserve a resource such as memory or disk. See memory allocation.  more of their cropland to maize and vegetables, and less to sugar and cotton. While in the Southeast region, farmers choose to have more of their cropland growing vegetables and rice, and less growing sugar and cotton. Each of the regions has a different cropping pattern because they have different regional conditions (Table 7). For example, the Northeast region is much colder than the other regions; its annual average temperature is 6.5[degrees]C, while it is warmer in both the Southeast and Southwest regions, their average annual temperature reaches 16[degrees]C. In addition, the Southeast region is wetter than the other regions with an average precipitation per month of 9.9cm, while it is very dry in the Northwest region
This article is about the region in Pennsylvania. For the area of the United States of America, see Pacific Northwest.

The Northwest Region
 with an average precipitation per month of only 2.2cm.

Each region responds in a different way to the climate scenarios (Table 8). For example, with the 2100 climate of the PCM model, farmers in Northwest and Southwest regions are more likely to irrigate their land, while farmers in the other three eastern regions (Northeast, Southeast and Middle regions) are less likely to irrigate. The national numbers hide these much larger changes in each region by averaging across all regions. Farmers will also adjust crop choice differently in each region. For example, with the 2100 PCM climate, farmers in the Southwest region will be less likely to grow wheat by -1%, while in the other four regions the likelihood of farmers growing wheat will increase by +4% to +14% depending on the climate scenario. Under the HADCM3 climate scenario, the likelihood of farmers in the Southeast, Middle and Northwest regions growing rice will increase by up to +2%, while the fraction of cropland devoted to rice in the Southeast and Southwest regions is reduced by -1% to -2%, depending on the future climate scenario. Similar variations in crop choice across regions apply to the other crops as well and for each climate change scenario.


The results of the adaptation analysis strongly suggest that Chinese farmers do adapt to climate by shifting to irrigation (where possible) and by switching crops. With warmer temperatures, they are more likely to increase irrigation and grow oil crops, wheat, and especially cotton. As precipitation increases, they may also increase irrigation and choose to grow more soybean, oil crops, and especially rice, cotton, and vegetables. However, all of these results are dependent on no changes in water flow. Since all of these decisions are likely to be affected by water flow and climate change is likely to change water flows, the results must be interpreted cautiously. It is clearly very important to follow this study with an additional analysis where water flow can be taken into account. (3)

The projections of changes in future climates must be viewed cautiously. First, the climate projections themselves are uncertain and range from mild to severe changes. Second, the impact analysis in this paper only examines changes in climate. There are many other changes that are likely to take place as well. There is likely to be new crop varieties, changes in relative prices, new management practices, and new technologies. Water availability is likely to change with and without climate change. All of these changes must be taken into account to obtain an accurate forecast of future conditions.

One point that is clear from the results concerning adaptation is that Chinese farmers already have adapted to climate. Climate affects irrigation choice and crop choice. Therefore, it is very likely that Chinese farmers will adapt to climate change. Examining a set of future climate scenarios, the model predicts there will be substantial changes in both the irrigation mix and the crop mix across China with climate change. The magnitude of the change will depend on the climate scenario. Future studies of the impacts of climate change on Chinese agriculture must take these adaptations into account. Failing to account for adaptation will overestimate the damages from climate change.

The analysis also revealed that adaptations will vary dramatically across regions. The analysis predicts that some regions will increase irrigation while other regions will move towards rainfed agriculture. Warming will also cause changing cropping patterns across China. Global warming global warming, the gradual increase of the temperature of the earth's lower atmosphere as a result of the increase in greenhouse gases since the Industrial Revolution.  will cause some crops to increase in one region and fall in another. Policy makers must be aware of this spatial variation of adaptation. They must resist the temptation Temptation
Terror (See HORROR.)


as fruit of the tree of knowledge in Eden, has come to epitomize temptation. [O.T.: Genesis 3:1–7; Br. Lit.
 to move towards nation wide adaptation policies and instead make sure that adaptation is sensible at the local level.

The analysis in this paper examined irrigation and crop choice. Farmers can take other measures as well in response to climate change. They can adjust varieties, not just species. They can alter when they plant and harvest. They can choose different tillage practices. They can adopt different irrigation technologies. They can adjust other inputs such as labor, capital, and fertilizer fertilizer, organic or inorganic material containing one or more of the nutrients—mainly nitrogen, phosphorus, and potassium, and other essential elements required for plant growth. . All of these measures need to be examined.

Farmers can select from an arsenal of adaptation alternatives. This implies that adaptation options have to be available. This is a policy matter. Government and the private sector may be in a position to prepare sets of adaptation options to be available for use by individual farmers, and to provide needed knowhow know´how`   

n. 1. the knowledge and skill required to do something; practical knowledge for a specific task.
 via efficient public extension services or private agents. The government could also help establish the background or prerequisites for private adaptation. That includes establishing accessible credit lines, and enforcing private property ownership.

Additional public adaptations are also needed at the government level. One of the most important adaptations that China can make concerns water management. By reallocating water to its best use, the government can make important adaptations. This includes sending signals of the economic value of water by establishing water markets or efficient quotas and/or regulatory policies. Water management may also involve engineering efforts to store water or transfer water from water abundant to water shortage regions. Another important adaptation concerns developing new crop varieties. If the government can develop new crops suited for a warmer world, it would provide farmers with new opportunities that could be very valuable in the future.

This paper--a product of the Sustainable Rural and Urban Development Team, Development Research Group--is part of a larger effort in the department to mainstream research on climate change. Policy Research Working Papers working papers
Legal documents certifying the right to employment of a minor or alien.

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(networking) org - The top-level domain for organisations or individuals that don't fit any other top-level domain (national, com, edu, or gov). Though many have .org domains, it was never intended to be limited to non-profit organisations.

RFC 1591.
. The authors may Be contacted at, robert Robert, Henry Martyn 1837-1923.

American army engineer and parliamentary authority. He designed the defenses for Washington, D.C., during the Civil War and later wrote Robert's Rules of Order (1876).

Noun 1.
. See .edu.

(networking) edu - ("education") The top-level domain for educational establishments in the USA (and some other countries). E.g. "". The UK equivalent is "".
, (After 12/1/2008,

We would like to thank Lijuan Zhang, Jianmin Cao CAO Chief Administrative Officer (corporate title)
CAO Conception Assistee Par Ordinateur (French)
CaO Calcium Oxide
CAO Collectieve Arbeidsovereenkomst (Dutch) 
, Cheng Chen, Yumin Li, Xiangjun Xing Xing Crossing  and Hao hao  
n. pl. hao
See Table at currency.

[Vietnamese hào.]

Noun 1.
 Li for their assistance in data cleaning. We acknowledge financial suport from DECRG in the World Bank, State Office of Comprehensive Agricultural Development (SOCAD) in China, and Global Environmental Foundation and China's National Natural Sciences Foundation (70733004) and Chinese Academy of Sciences The Chinese Academy of Sciences (CAS) (Simplified Chinese: 中国科学院; Pinyin: Zhōngguó Kēxuéyuàn), formerly known as Academia Sinica  (KSCX-YW-09). We acknowledge the constructive comments by Carter Brandon Brandon, city, Canada
Brandon, city (1991 pop. 38,567), SW Man., Canada, on the Assiniboine River. The business center of the wheat-raising area of SW Manitoba, Brandon has an extensive trade in farm products and machinery.
. The views expressed in this paper are those of the authors and should not be attributed to any of the funding sources.


IPCC. 2007. Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change “IPCC” redirects here. For other uses, see IPCC (disambiguation).
The Intergovernmental Panel on Climate Change (IPCC) was established in 1988 by two United Nations organizations, the World Meteorological Organization (WMO) and the United Nations Environment
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, UK.

Kurukulasuriya, P., R. Mendelsohn, R. Hassan Hassan (häsän`), d. 1894, sultan of Morocco (1873–94). He brought the weak and disorganized country firmly under his control and held in check attempts by European powers at domination. , J. Benhin, M. Diop, H. M. Eid, K.Y. Fosu, G. Gbetibouo, S. Jain Jain   also Jai·na
A believer or follower of Jainism.

[Hindi jaina, from Sanskrit jaina-, relating to the saints, from jina
, , A. Mahamadou, S. El-Marsafawy, S. Ouda, M. Ouedraogo, I. Sene, N. Seo, D. Maddison and A. Dinar, 2006. "Will African Agriculture Survive Climate Change?" World Bank Economic Review, 20: 367-388.

Kurukulasuriya, P. and R. Mendelsohn, 2007. "Modeling Endogenous Irrigation: The Impact of Climate Change on Farmers in Africa". World Bank Policy Research Working Paper 4278.

Kurukulasuriya, P. and R. Mendelsohn. 2008. "A Ricardian Analysis of the Impact of Climate Change on African Cropland" African Journal Agriculture and Resource Economics 2:1-23.

Kurukulasuriya, P. and R. Mendelsohn. 2008. "Crop Switching as an Adaptation Strategy to Climate Change" African Journal Agriculture and Resource Economics 2: 105-126.

Leary, N., MW. Baethgen, V. Barros, I. Burton, O. Canziani, TE. Downing, R. Klein Klein , Melanie 1882-1960.

Austrian-born British psychoanalyst who first introduced play therapy and was the first to use psychoanalysis to treat young children.
, D. Malpede, JA. Marengo, LO. Mearns, RD. Lasco, and SO. Wandiga. 2006. "A Plan of Action to Support Climate Change Adaptation through Scientific Capacity, Knowledge and Research" AIACC Report 23, International START Secretariat Secretariat, 1970–89, thoroughbred race horse. Trained by Lucien Laurin and ridden by Ron Turcotte, Secretariat won the Kentucky Derby, Preakness, and Belmont Stakes to capture the Triple Crown in 1973.

(foaled 1970) U.S.
, Washington DC.

McCarthy, James, Canziani, Osvaldo F., Leary, Neil A., Dokken, David J. and White, Casey, (eds.) Climate Change 2001: Impacts, Adaptation, and Vulnerability, Cambridge: Cambridge University Press, Intergovernmental Panel on Climate Change, 2001.

McFadden, D. 1981. "Econometric e·con·o·met·rics  
n. (used with a sing. verb)
Application of mathematical and statistical techniques to economics in the study of problems, the analysis of data, and the development and testing of theories and models.
 Models of Probabilistic Choice", in D. McFadden, Structural Analysis of Discrete Data and Econometric Applications, Cambridge, MIT MIT - Massachusetts Institute of Technology  Press.

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Mendelsohn, R., A. Dinar, and A. Sanghi, 2001. "The Effect of Development on the Climate Sensitivity of Agriculture" Environment and Development Economics, 6: 85-101.

Mendelsohn, R. and A. Dinar, 2003. "Climate, Water, and Agriculture" Land Economics, 79: 328-341.

Parry, M.L., C. Rosenzweig, A. Iglesias, M. Livermore, G. Fischer, 2004. "Effects of Climate Change on Global Food Production under SRES SRES Seniors Real Estate Specialist
SRES Special Report on Emission Scenarios (Intergovernmental Panel on Climate Change)
SRES Senate Resolution
SRES Signed Response
SRES Surgically Remediable Epilepsy Syndromes
SRES System Resource
 Emissions and Socioeconomic Scenarios" Global Environmental Change, 14, 53-67.

Reilly, J., et al. 1996. "Agriculture in a Changing Climate: Impacts and Adaptations" in Watson, R., M. Zinyowera, R. Moss, and D. Dokken (eds.) Climate Change 1995: Impacts, Adaptations, and Mitigation MITIGATION. To make less rigorous or penal.
     2. Crimes are frequently committed under circumstances which are not justifiable nor excusable, yet they show that the offender has been greatly tempted; as, for example, when a starving man steals bread to satisfy
 of Climate Change: Scientific-Technical Analyses, Intergovernmental Panel on Climate Change, Cambridge University Press: Cambridge p427-468.

Rosenzweig, C. and M. Parry, 1994. Potential Impact of Climate Change on World Food Supply. Nature, 367: 133-38.

Seo, S. N. and R. Mendelsohn. 2008a. "A Ricardian Analysis of the Impact of Climate Change on South American Farms", Chilean Journal Of Agricultural Research 68(1): 69-79.

Seo, S. N. and R. Mendelsohn. 2008b. "An Analysis of Crop Choice: Adapting to Climate Change in South American Farms", Ecological Economics Ecological economics is a transdisciplinary field of academic research that addresses the dynamic and spatial interdependence between human economies and natural ecosystems.  65: 508-515.

Seo, N. and R. Mendelsohn. 2008c. "Climate Change Impacts and Adaptations on Animal Husbandry animal husbandry, aspect of agriculture concerned with the care and breeding of domestic animals such as cattle, goats, sheep, hogs, and horses. Domestication of wild animal species was a crucial achievement in the prehistoric transition of human civilization from  in Africa" African Journal Agriculture and Resource Economics 2: 65-82.

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(1) One should distinguish between adaptation to climate change, which spans over a long period, which differs from adaptation to climate variance The discrepancy between what a party to a lawsuit alleges will be proved in pleadings and what the party actually proves at trial.

In Zoning law, an official permit to use property in a manner that departs from the way in which other property in the same locality
, the changes in weather from year to year (Leary et al. 2006). Adapting to weather is important but it should not be confused with adapting to climate.

(2) The Northeast region includes Liaoning, Jilin, Heilongjiang, Tianjin and Hebei provinces; the Southeast region includes Shanghai Shanghai (shăng`hī`, shäng`hī`), city (1994 est. pop. 12,980,000), in, but independent of, Jiangsu prov., E China, on the Huangpu (Whangpoo) River where it flows into the Chang (Yangtze) estuary. , Jiangsu, Zhejiang, Fujian, Shandong and Guangdong provinces; the Middle region includes Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan, Inner Mongolia Inner Mongolia
 Chinese Nei Mongol or Nei-meng-ku

Autonomous region (pop., 2002 est.: 23,790,000), China. Stretching some 1,800 mi (2,900 km) across north-northeastern China, it has an area of 454,600 sq mi (1,177,500 sq km); its capital is Hohhot.
 and Guangxi provinces; the Northwest region includes Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang provinces; and the Southwest region includes Chongqing, Sichuan, Guizhou and Yunnan provinces.

(3) Such a study may need hydrological hy·drol·o·gy  
The scientific study of the properties, distribution, and effects of water on the earth's surface, in the soil and underlying rocks, and in the atmosphere.
 modeling of the river basin within which the observed farms are located.

Jinxia Wang is an Associate Professor in the Center for Chinese Agricultural Policy Agricultural policy describes a set of laws relating to domestic agriculture and imports of foreign agricultural products. Governments usually implement agricultural policies with the goal of achieving a specific outcome in the domestic agricultural product markets.  (CCAP), Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing, China ( Robert Mendelsohn Robert Mendelsohn may refer to:
  • Robert O. Mendelsohn, American economist
  • Robert S. Mendelsohn, American pediatrician
 is Edwin Weyerhaeuser Davis Professor in the School of Forestry and Environmental Studies in the Yale University Yale University, at New Haven, Conn.; coeducational. Chartered as a collegiate school for men in 1701 largely as a result of the efforts of James Pierpont, it opened at Killingworth (now Clinton) in 1702, moved (1707) to Saybrook (now Old Saybrook), and in 1716 was , USA, ( Ariel Dinar is a Lead Economist in the Development Research Group of the World Bank, Washington DC, USA ( Jikun Huang is Director and Professor of CCAP (
Table 1: Regression of Irrigation Choice

                                       If irrigate (1=Yes; 0=No)

Variables                          Without household   With household
                                       variables          variables

                                       Quadratic          Quadratic

Spring temp                             -1.2439            -1.3439
                                      (8.83) ***          (9.30) ***
Spring temp sq                          0.0293              0.0314
                                      (6.75) ***          (7.11) ***
Summer temp                             0.6607              0.6795
                                      (3.28) ***          (3.31) ***
Summer temp sq                          -0.0008            -0.0009
                                         -0.17              -0.19
Fall temp                               0.2180              0.3189
                                       (1.68) *           (2.42) **
Fall temp sq                            -0.0277            -0.0316
                                      (5.18) ***          (5.81) ***
Winter temp                             0.6087              0.6319
                                      (8.75) ***          (8.92) ***
Winter temp sq                          0.0086              0.0102
                                      (4.50) ***          (5.19) ***
Spring prec                             -0.7896            -0.7931
                                      (8.02) ***          (8.02) ***
Spring prec sq                          0.0343              0.0345
                                      (8.80) ***          (8.82) ***
Summer prec                             -0.1559            -0.1826
                                      (4.92) ***          (5.59) ***
Summer prec sq                          0.0003              0.0008
                                        (0.29)              (0.82)
Fall prec                               -0.1123            -0.1199
                                        (1.25)              (1.33)
Fall prec sq                            0.0071              0.0074
                                        (1.63)             (1.71) *
Winter prec                             1.2560              1.2345
                                      (7.47) ***          (7.31) ***
Winter prec sq                          -0.1455            -0.1432
                                      (7.02) ***          (6.88) ***
Share of land areas with                1.1462              1.1690
clay soil                             (7.47) ***          (7.54) ***
Share of land areas with                0.1272              0.1395
silt soil                               (1.01)              (1.11)
Plain (1=Yes; 0=No)                     0.5169              0.4861
                                      (6.49) ***          (6.09) ***
Road (1=Yes; 0=No)                      0.8485              0.8304
                                      (5.49) ***          (5.34) ***
Distance to township government         -0.0548            -0.0492
                                      (8.63) ***          (7.65) ***
If participate production                                   0.8579
association (1=Yes; 0=No)                                 (3.88) ***
Share of labor without                                     -0.0030
receiving education                                        (1.93) *
Cultivated land area per                                   -0.1438
household                                                 (4.27) ***
Constant                                1.6391              1.928
Pseudo [R.sup.2]                        (0.97)              (1.11)
                                         0.19                0.2
Observations                             8405                8405

Values in parenthesis are t-statistics. One asterisk implies
significant at 5% level and two asterisks implies significant at 1%

Table 2 Marginal effect of climate change on irrigation choice

Without household variables     Temperature        Precipitation
                                [degrees]C             cm/mo

Spring                            -0.2110             -0.3647
Summer                             0.0751             -0.0245
Fall                               0.0414             -0.0151
Winter                            -0.0001              0.1218
Annual                            -0.0204             -0.1217

With household variables

Spring                            -0.2310             -0.3654
Summer                             0.0758             -0.0295
Fall                               0.0540             -0.0161
Winter                            -0.0130              0.1197
Annual                            -0.0276             -0.1354

Note: Marginal effects calculated using regressions coefficients from
Table 1 evaluated at each climate point in sample.

Table 3 Multinomial logit regression of crop choice

                              Wheat         Rice        Vegetable

Spring temp                   0.119         0.27         -0.025
                             (-0.82)      (1.84) *       (-0.25)
Spring temp sq               -0.013        -0.0048       -0.0034
                           (2.86) ***      (-1.07)       (-1.11)
Summer temp                  -1.027        -0.681        -0.125
                           (6.22) ***    (2.82) ***      (-0.88)
Summer temp sq                0.031        0.0078        0.0042
                           (8.06) ***      (-1.57)       (-1.32)
Fall temp                    -0.118         1.05          0.177
                             (-0.90)     (7.11) ***     (1.88) *
Fall temp sq                 -0.014        -0.024        -0.0113
                            (2.81) **    (5.07) ***    (3.13) ***
Winter temp                   0.347        -0.294         0.111
                           (6.83) ***    (4.46) ***     (2.50) **
Winter temp sq               0.0004        0.0094         0.007
                             (-0.20)     (4.94) ***    (5.27) ***
Spring prec                   0.831         0.11          -0.08
                           (8.43) ***     (1.75) *       (-1.40)
Spring prec sq               -0.048        -0.0013       0.0038
                           (10.63) ***     (-0.58)      (1.81) *
Summer prec                  -0.213        -0.038        -0.108
                           (7.69) ***      (-1.32)     (5.19) ***
Summer prec sq               0.00676       0.0003        0.0023
                           (7.17) ***      (-0.36)     (3.59) ***
Fall prec                    -0.522         0.416         0.129
                           (6.59) ***    (5.57) ***     (2.21) **
Fall prec sq                 0.0257        -0.0203       -0.0098

                           (6.47) ***    (5.76) ***    (3.47) ***
Winter prec                  -0.173         0.892         0.892
                             (-1.10)     (7.13) ***    (8.12) ***
Winter prec sq               0.0694        -0.0603       -0.057
                           (3.55) ***    (4.20) ***    (4.40) ***
Share of land areas with      -0.69        -0.913        -0.528
clay soil                  (5.09) ***    (8.03) ***    (5.14) ***
Share of land areas with     -0.243         -0.67        -0.026
silt soil                   (2.77) **    (5.82) ***      (-0.32)
Plain (1=Yes; 0=No)          -0.0412       -0.0968       -0.0028
                             (-0.59)       (-1.53)       (-0.05)
Road (1=Yes; 0=No)            0.362         0.449         0.376
                           (3.03) ***    (4.11) ***    (3.81) ***
Distance to township         0.0115        -0.0098       -0.0091
government                  (2.15) **     (1.86) *      (2.07) **
Share of irrigated areas     0.00501        0.005        0.0007
in village                 (6.40) ***    (6.32) ***      (-1.05)
If participating in a         0.076         0.074        -0.116
production association       (-0.55)       (-0.57)       (-0.95)
Share of labors without      0.0005        0.0016         0.001
receiving education          (-0.38)       (-1.28)       (-0.94)
Cultivated land area per      0.232        -0.164        -0.0967
household                  (7.22) ***    (3.84) ***    (3.53) ***
Constant                      11.64         -4.21         0.92
                           (7.91) ***     (1.93) *        -0.74

                             Soybean       Potato        Cotton

Spring temp                   -1.21         0.229        -1.191
                           (8.84) ***     (2.03) **     (2.20) **
Spring temp sq               0.0313        -0.0177       0.0158
                           (7.05) ***    (5.12) ***      (-0.77)
Summer temp                   2.446         0.237        -2.541
                           (10.03) ***     (-1.53)      (2.45) **
Summer temp sq               -0.0427       -0.0034       0.0903
                           (8.37) ***      (-0.98)     (4.17) ***
Fall temp                    -0.458        -0.445        -0.589
                           (3.70) ***    (4.03) ***      (-0.85)
Fall temp sq                 -0.0016       0.0094        0.0025
                             (-0.34)      (2.31) **      (-0.09)
Winter temp                   0.365         0.242        -0.307
                           (5.94) ***    (4.54) ***      (-1.42)
Winter temp sq               -0.0023       0.0073        -0.0972
                             (-1.17)     (4.79) ***    (5.79) ***
Spring prec                   0.27          0.162         0.447
                           (3.64) ***     (2.52) **     (1.84) *
Spring prec sq               -0.0127       -0.0017       -0.0002
                           (4.78) ***      (-0.72)       (-0.02)
Summer prec                   0.021        -0.075        -0.253
                             (-0.70)     (2.85) ***    (2.81) ***
Summer prec sq               -0.0002       0.0002        -0.0028
                             (-0.18)       (-0.28)       (-0.66)
Fall prec                     0.218         0.247        -0.799
                           (2.97) ***    (3.59) ***    (3.61) ***
Fall prec sq                 -0.0271       -0.016        0.0479

                           (7.62) ***    (4.88) ***    (3.92) ***
Winter prec                   0.194         0.201         2.771
                             (-1.33)       (-1.56)     (4.91) ***
Winter prec sq               0.0582        -0.0051       -0.3558
                           (3.45) ***      (-0.35)     (5.52) ***
Share of land areas with     -0.382         -0.2          0.873
clay soil                  (2.82) ***     (1.66) *     (2.77) ***
Share of land areas with      0.055         0.706        -0.207
silt soil                    (-0.50)     (6.63) ***      (-1.20)
Plain (1=Yes; 0=No)          -0.0761       -0.3192       1.6194
                             (-1.13)     (4.95) ***    (9.59) ***
Road (1=Yes; 0=No)            0.002         0.427        -0.222
                             (-0.01)     (3.79) ***      (-1.07)
Distance to township         -0.0021       0.0004        -0.0166
government                   (-0.39)       (-0.09)       (-1.39)
Share of irrigated areas     -0.0014       -0.004        -0.0004
in village                   (-1.64)     (4.77) ***      (-0.32)
If participating in a        -0.291         0.278         1.052
production association      (1.72) *      (1.96) *     (5.17) ***
Share of labors without      0.0037        0.0022        0.0026
receiving education         (2.60) **     (1.80) *       (-1.13)
Cultivated land area per      0.009         0.06          0.422
household                    (-0.31)      (1.98) *     (5.37) ***
Constant                     -19.87         -1.68         27.2
                           (9.67) ***      (-1.29)      (2.88) **

                            Oil Crops       Sugar

Spring temp                  -0.176        -0.181
                             (-1.38)       (-0.46)
Spring temp sq               -0.0094       0.0015
                            (2.39) **      (-0.13)
Summer temp                   0.436         1.457
                            (2.56) **     (2.24) **
Summer temp sq               0.0002        -0.0225
                             (-0.07)       (-1.56)
Fall temp                     -0.9         -1.411
                           (7.09) ***    (3.22) ***
Fall temp sq                 0.0118        0.0044
                            (2.67) **       -0.31
Winter temp                   0.542         0.813
                           (9.92) ***    (4.94) ***
Winter temp sq               -0.0001       0.0136
                             (-0.06)      (2.26) **
Spring prec                  -0.189         0.64
                           (2.84) ***    (3.40) ***
Spring prec sq               0.0056        -0.0314
                            (2.31) **    (4.92) ***
Summer prec                  -0.0052       -0.009
                             (-0.20)       (-0.12)
Summer prec sq               -0.0008        0.001
                             (-1.01)       (-0.53)
Fall prec                    -0.223        -1.026
                           (3.23) ***    (3.93) ***
Fall prec sq                 0.0083        0.0184

                           (2.53) ***      (-1.41)
Winter prec                   1.293         1.198
                           (9.92) ***    (2.58) ***
Winter prec sq               -0.0946       0.0177
                           (6.22) ***      (-0.35)
Share of land areas with      -0.39         0.801
clay soil                  (3.36) ***     (2.26) **
Share of land areas with     -0.194        -0.894
silt soil                   (1.97) *      (2.28) **
Plain (1=Yes; 0=No)          -0.3589       0.6544
                           (5.56) ***    (4.20) ***
Road (1=Yes; 0=No)           0.2709        0.0917
                            (2.42) **      (-0.29)
Distance to township         -0.0115       -0.0182
government                  (2.18) **      (-1.29)
Share of irrigated areas     0.0014         0.006
in village                  (1.82) *      (2.48) **
If participating in a         0.162        -0.433
production association       (-1.22)       (-1.12)
Share of labors without      0.0034        -0.0033
receiving education         (2.79) **      (-0.85)
Cultivated land area per      0.305         0.26
household                  (10.05) ***   (4.17) ***
Constant                      2.74          -6.7
                            (1.92) *       (-1.17)

Note: Absolute value of z statistics in parentheses. Maize is the
omitted choice. There are 8405 observations. The LR chi2 of the
regression is13347 and the Pseudo R squared is 0.1034.

Table 4 Marginal effect of climate change on crop choice

                           Change of probability of choosing crops

                              Wheat         Rice      Vegetable
Temperature ([degrees]C)

Spring                       -0.0073       0.0417       0.0067
Summer                        0.0172      -0.0527      -0.0339
Fall                         -0.0293       0.0657       0.0040
Winter                        0.0157      -0.0453      -0.0143
Annual                        0.0090      -0.0092      -0.0054

Precipitation (cm/mo)

Spring                        0.0274       0.0057      -0.0170
Summer                        0.5562      -0.0665      -0.1269
Fall                         -0.0176       0.0160       0.0064
Winter                       -0.0378       0.0012       0.0238
Annual                       -0.0327       0.0326       0.0237

                           Change of probability of choosing crops

                             Soybean       Potato       Cotton
Temperature ([degrees]C)

Spring                       -0.0161      -0.0116      -0.0113
Summer                        0.0127      -0.0134       0.0860
Fall                         -0.0254       0.0005      -0.0063
Winter                        0.0177       0.0073      -0.0056
Annual                       -0.0113      -0.0052       0.0168

Precipitation (cm/mo)

Spring                        0.0003       0.0083       0.0075
Summer                       -0.0500      -0.1160      -0.0190
Fall                         -0.0081       0.0055      -0.0068
Winter                        0.0156      -0.0260       0.0382
Annual                        0.0100      -0.0128       0.0266

                           Change of probability of choosing crops

                            Oil Crops      Sugar        Maize
Temperature ([degrees]C)

Spring                       -0.0299       0.0002       0.0276
Summer                        0.0225       0.0009       0.0009
Fall                         -0.0338      -0.0066      -0.0066
Winter                        0.0445       0.0091       0.0091
Annual                        0.0048      -0.0014       0.0019

Precipitation (cm/mo)

Spring                       -0.0167       0.0000      -0.0155
Summer                       -0.0639      -0.0053      -0.0053
Fall                          0.0042      -0.0053      -0.0053
Winter                        0.0361       0.0141       0.0141
Annual                        0.0110       0.0021      -0.0602

Note: Marginal effects calculated using coefficients from Table 3,
evaluated at each climate point in sample.

Table 5 Simulation results on the annual effect of climate change on
crop and irrigation choice


                                          PCM       HADCM3      CCM2

Change of temp. ([degrees]C)              0.74       1.08       1.5
Change of Prec. (%)                       3.44      -1.31       0.18
Change of probability of choosing
          Wheat                          0.0062     0.0100     0.0131
          Rice                          -0.0010    -0.0123    -0.0143
          Vegetable                     -0.0024    -0.0065    -0.0092
          Soybean                       -0.0077    -0.0125    -0.0170
          Potato                        -0.0039    -0.0056    -0.0079
          Cotton                         0.0092     0.0196     0.0280
          Oil Crops                      0.0004     0.0063     0.0073
          Sugar                         -0.0019    -0.0011    -0.0018
          Maize                          0.0012     0.0021     0.0019
Change of probability of
choosing irrigation
          Without household variables   -0.2299    -0.1766    -0.1649
          With household variables      -0.1951    -0.1443    -0.1310


                                          PCM       HADCM3      CCM2

Change of temp. ([degrees]C)              2.45       4.01       4.1
Change of Prec. (%)                       8.23       7.69      -1.71
Change of probability of choosing
          Wheat                          0.0202     0.0283     0.0273
          Rice                          -0.0139    -0.0372    -0.0508
          Vegetable                     -0.0141    -0.0349    -0.0437
          Soybean                       -0.0269    -0.0429    -0.0439
          Potato                        -0.0136    -0.0236    -0.0253
          Cotton                         0.0466     0.1042     0.1215
          Oil Crops                      0.0057     0.0176     0.0265
          Sugar                         -0.0041    -0.0042    -0.0019
          Maize                          0.0002    -0.0074    -0.0096
Change of probability of
choosing irrigation
          Without household variables   -0.1665    -0.0763    -0.0231
          With household variables      -0.1263    -0.0370     0.0096

Note: 1) The base year is 1990-2000;

2) Climate scenario for A2 Emissions Scenario.Data for each model is
available at

Table 6 Irrigation and crop choice of farmers under current climate

                                        Five regions

                              Northeast   Southeast    Middle

Share of irrigated areas (%)     25          68          53
Share of crop sown areas (%)
         Wheat                    7          11           9
         Rice                     8          24          15
         Maize                   28           9          17
         Soybean                 14           6           9
         Patato                  10           7           9
         Cotton                   2           3           2
         Oil crops                4          12          14
         Sugar                    0           1           1
         Vegetables              26          27          25
         Total                   100         100         100

                                  Five regions

                              Northwest   Southwest

Share of irrigated areas (%)     60          30
Share of crop sown areas (%)
         Wheat                   28          10
         Rice                     1          17
         Maize                   17          21
         Soybean                  3           6
         Patato                  12          17
         Cotton                   7           0
         Oil crops               14           8
         Sugar                    1           1
         Vegetables              17          22
         Total                   100         100

Note: 1) The Northeast region includes Liaoning, Jilin, Heilongjiang,
Tianjin and Hebei provinces;

2) The Southeast region includes Shanghai, Jiangsu, Zhejiang, Fujian,
Shandong and Guangdong provinces;

3) The Middle region includes Shanxi, Anhui, Jiangxi, Henan, Hubei,
Hunan, Inner Mongolia and Guangxi provinces;

4) The Northwest region includes Shaanxi, Gansu, Qinghai, Ningxia and
Xinjiang provinces;

5) The Southwest region includes Chongqing, Sichuan, Guizhou and
Yunnan provinces.

Table 7 Current climate condition by region

                                        Five regions

                              Northeast   Southeast    Middle
Temp. ([degrees]C)
                     Spring      7.6        15.5        15.1
                     Summer     22.0        26.4        25.9
                     Fall        7.5        17.6        15.9
                     Winter    -11.0         5.0         3.2
                     Annual      6.5        16.1        15.0
Prec. (cm/mo)
                     Spring      2.6        10.2        11.7
                     Summer     12.6        18.1        15.3
                     Fall        3.4         7.4         5.9
                     Winter      0.5         3.9         3.8
                     Annual      4.8         9.9         9.2

                         Five regions

                     Northwest   Southwest
Temp. ([degrees]C)
                       10.2        16.4
                       21.2        24.2
                        8.3        16.6
                       -5.8         7.1
                        8.5        16.0
Prec. (cm/mo)
                        1.6         8.6
                        4.8        18.0
                        2.0         9.0
                        0.2         1.8
                        2.2         9.3

Note: 1) The Northeast region includes Liaoning, Jilin, Heilongjiang,
Tianjin and Hebei provinces;

2) The Southeast region includes Shanghai, Jiangsu, Zhejiang, Fujian,
Shandong and Guangdong provinces;

3) The Middle region includes Shanxi, Anhui, Jiangxi, Henan, Hubei,
Hunan, Inner Mongolia and Guangxi provinces;

4) The Northwest region includes Shaanxi, Gansu, Qinghai, Ningxia and
Xinjiang provinces;

5) The Southwest region includes Chongqing, Sichuan, Guizhou and
Yunnan provinces.

Table 8 Simulation results on predicting changes in crop and irrigation
choice by region for different climate scenarios in 2090-2100

Model        Change of      Change    Change of probability of
               temp.       of Prec.        choosing crops

            ([degrees]C)     (%)        Wheat         Rice

PCM             2.45         8.23       0.0778       0.0244
HADCM3          4.01         7.69       0.1314       0.0114
CCM2            4.1         -1.71       0.1270      -0.0025
PCM             2.45         8.23       0.0859       0.0366
HADCM3          4.01         7.69       0.1196      -0.0137
CCM2            4.1         -1.71       0.1454      -0.0494
PCM             2.45         8.23       0.0681       0.0528
HADCM3          4.01         7.69       0.0928       0.0193
CCM2            4.1         -1.71       0.1434      -0.0192
PCM             2.45         8.23       0.0352       0.0026
HADCM3          4.01         7.69       0.0539       0.0002
CCM2            4.1         -1.71       0.0549      -0.0013
PCM             2.45         8.23      -0.0120       0.0028
HADCM3          4.01         7.69      -0.0113      -0.0232
CCM2            4.1         -1.71      -0.0106      -0.0447

Model           Change of probability of choosing crops

            Vegetable     Soybean       Potato       Cotton

PCM          -0.0656      -0.0414       0.0076      -0.0056
HADCM3       -0.1124      -0.0628       0.1221      -0.0125
CCM2         -0.1217      -0.0665       0.1844      -0.0127
PCM          -0.0342      -0.0154       0.0019       0.0042
HADCM3       -0.0524      -0.0174       0.0016       0.0431
CCM2         -0.0559      -0.0179       0.0022       0.0596
PCM          -0.0441      -0.0651       0.1393      -0.0297
HADCM3       -0.0673      -0.0731       0.1676      -0.0097
CCM2         -0.0751      -0.0804       0.1949      -0.0121
PCM           0.0032      -0.0302       0.0614      -0.0308
HADCM3       -0.0101      -0.0551       0.1888      -0.0557
CCM2         -0.0137      -0.0589       0.2154      -0.0573
PCM           0.0155      -0.0084     -0.0000003     0.0022
HADCM3        0.0139      -0.0147     -0.0000003     0.0164
CCM2          0.0223      -0.0111     -0.0000003     0.0225

Model         Change of probability of       Change of probability of
                   choosing crops               choosing irrigation

            Oil Crops    Sugar      Maize      Without      With hh
Northeast                                        hh.       variables

PCM          -0.0026    -0.0320     0.0375     -0.0749      -0.0993
HADCM3       -0.0030    -0.0784     0.0042     -0.0484      -0.0845
CCM2         -0.0030    -0.0892    -0.0159      0.0534       0.0246
PCM          -0.0007    -0.0541    -0.0242     -0.0690      -0.0864
HADCM3       -0.0013    -0.0649    -0.0144      0.0113      -0.0029
CCM2         -0.0011    -0.0751    -0.0079      0.0181       0.0060
PCM          -0.0010    -0.0826    -0.0378     -0.0791      -0.0942
HADCM3       -0.0030    -0.0950    -0.0315     -0.0197      -0.0336
CCM2         -0.0027    -0.1182    -0.0307     -0.0016      -0.0111
PCM          -0.0042    -0.0303    -0.0068      0.0363       0.0313
HADCM3       -0.0056    -0.0702    -0.0463      0.0729       0.0663
CCM2         -0.0056    -0.0767    -0.0569      0.0937       0.0882
PCM         -0.00005    -0.0126     0.0126      0.0862       0.0822
HADCM3       0.00004    -0.0150     0.0339      0.2050       0.2029
CCM2         0.00057    -0.0171     0.0381      0.1967       0.1951

Note: 1) The base year is 1990-2000; 2) Climate scenario for A2
Emissions Scenario. Data for each model is available at 3) The Northeast region
includes Liaoning, Jilin, Heilongjiang, Tianjin and Hebei provinces;
the Southeast region includes Shanghai, Jiangsu, Zhejiang, Fujian,
Shandong and Guangdong provinces; the Middle region includes Shanxi,
Anhui, Jiangxi, Henan, Hubei, Hunan, Inner Mongolia and Guangxi
provinces; the Northwest region includes Shaanxi, Gansu, Qinghai,
Ningxia and Xinjiang provinces; and the Southwest region includes
Chongqing, Sichuan, Guizhou and Yunnan provinces.
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Title Annotation:Policy Research Working Paper 4758
Author:Wang, Jinxia; Mendelsohn, Robert; Dinar, Ariel; Huang, Jikun
Publication:How China's Farmers Adapt To Climate Change
Date:Oct 1, 2008

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