Can China continue feeding itself? The impact of climate change on agriculture.I. Introduction
For quite some time, global food security issues have been in the center of a policy debate in the economic literature. One of the major aspects of this debate has been the role of China, a giant economy currently with a population of 1.30 billion, 20% of the world's population, and with expected population growth rate of 1.2-2.3 percent per year into the next decade (CIA CIA: see Central Intelligence Agency.
(1) (Confidentiality Integrity Authentication) The three important concerns with regards to information security. Encryption is used to provide confidentiality (privacy, secrecy). 2007). China's share in the world's production of primary agricultural commodities is significant, mainly in grains, 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 , and cotton. In 2003, China's share was 15, 30, 17, 19, and 31 percent for wheat, rice, maize maize: see corn. , soybean, and cotton, respectively (Winters and Yusuf, 2007:16). According to according to
1. As stated or indicated by; on the authority of: according to historians.
2. In keeping with: according to instructions.
3. Census data (CNBS CNBS Comisión Nacional de Bancos Y Seguros (Honduras)
CNBS Cognitive, Neural, and Behavioral Science , 2001), shares of these crops in five of China's provinces (Hebei, Henan, Shandong, Anhui and Jiangsu), considered the bread basket bread basket
an agricultural area, such as the U.S. Midwest, that provides large amounts of food to other areas. [Am. Hist.: Misc.]
See : Farming of China, range between 70-80 percent of the area sown sown
A past participle of sow1.
Adj. 1. sown - sprinkled with seed; "a seeded lawn"
planted - set in the soil for growth .
With projected increases in population and standard of living in China, feeding larger numbers of more affluent people could become a challenge if not accompanied by increased supply (Paarlberg (1997). Several studies provide grain production projections into the not so distant future, but variations in the estimates are quite wide. Fan and Agcaoili-Sombilla (1997) compare several studies with projections of grain production in China (Brown 1995; Rosegrant, Agcaoili-Sombilla, and Perez, 1995; Huang, Rozelle, and Rosegrant 1997; Tuan 1994; Mitchell Mitchell, city (1990 pop. 13,798), seat of Davison co., SE S.Dak.; inc. 1881. Mitchell is a trade, distribution, and shipping center for a dairy and livestock area. and Ingco, 1993; and OECF OECF Overseas Economic Cooperation Fund (Japan)
OECF Opto-Electronic Conversion Function (sensors) , 1995). The reasons for the differences in projections among these studies are beyond the scope of this paper. However, one common feature of all these studies is that they do not take into account the potential effect of future climate change on agricultural production.
As scientific evidence becomes more convincing that rising greenhouse gases will warm the planet (IPCC See IMS Forum. 2007), it has become ever more important to understand the impacts of 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. . The impacts to the agriculture sector from climate change are among the largest and best documented. Agronomic a·gron·o·my
Application of the various soil and plant sciences to soil management and crop production; scientific agriculture.
ag studies suggest that crop yields may fall if the same crops are grown in the same places under various climate change scenarios (Reilly et al. 1996, McCarthy et al 2001). Studies applying the Ricardian Approach in Africa (Kurukulasuriya et al 2006) 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. (Seo and Mendelsohn 2007) suggest that warming will reduce farm net revenues. However, no single country is more important than China in terms of the number of people at risk and the impact on the world economy that may result from future climate change. Will China continue to be able to feed itself as the climate warms?
Many agronomic modeling studies have assessed the impacts of climate change on several grain crops (e.g., rice, maize, wheat) in various regions of China. The general findings of these studies are that crop yields will fall in China like those in other developing countries (e.g., Matthews and Wassmann, 2003; Parry et al., 2004; Tao et al., 2006; Wu et al., 2006; Xiong et al., 2007; Yao et al., 2007). These and other crop modeling studies have the same caveat in that they assume the same crops are grown in the same places as climate changes. Further, crop modeling studies in China do not include any economic values attached to the estimated yield reductions. And, there are no agro-economic models (such as Adams et al., 1995) that convert crop modeling results into economic outcomes for China.
The only economic study in China to date of the effect of warming on agriculture is a Ricardian analysis (Liu et al. 2004). Curiously, this study finds that warming will increase average farm net revenue, not reduce it. However, this Ricardian study is based on county level data with severe data limitations. Therefore, it is difficult to weigh the results of this study in comparison to the results of the host of crop studies that suggest that warming is harmful. Thus, there is not sufficient evidence to determine whether China can continue feeding itself given global warming.
To answer this question, this paper reports the results of a new study that measures the sensitivity of Chinese agriculture to warming, employing farm level data. Like the Liu et al., (2004) study, the analysis in this paper relies on the Ricardian method (Mendelsohn, et al., MNS MNS Minutes
MNS Maharashtra Navnirman Sena
MNS Malaysian Nature Society
MNS Mass Notification System
MNS Mirror Neuron System
MNS Metis Nation of Saskatchewan
MNS mission needs statement (US DoD)
MNS Maître Nageur Sauveteur 1994). The analysis is conducted on 8,405 farms sampled across 28 provinces. The data include information on each farm's economic operations, locational data, and other farm characteristics. Net revenue per hectare hectare (hĕk`târ, –tär), abbr. ha, unit of area in the metric system, equal to 10,000 sq m, or about 2.47 acres. is regressed on climate and a number of other exogenous Exogenous
Describes facts outside the control of the firm. Converse of endogenous. control variables. Matching the location to climate data (rainfall and temperature) and soils, it is possible to examine the effect of climate on net revenue controlling for many other factors.
The available data allow us to measure econometrically the direct effects of temperature and precipitation on crop net revenues. Unfortunately, the amount of 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. water a farmer uses is not available in the dataset. Although we know whether each farm is irrigated or not, we do not know water availability or cost. If future climate scenarios reduce available water supplies, this is likely to have an important harmful effect on China's agriculture that this study does not take into account. The analysis does not capture the indirect effect of climate change on crop net revenues through the supply of irrigation water and should be addressed in future studies.
The paper is organized as follows. We briefly review the methodology of the Ricardian method in the next section. Section III discusses the available data and the construction of the data set. In the Section IV, 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 and simulation of national and regional impacts for marginal changes in climate. The paper concludes with a summary of the key results, a discussion of policy relevance, and suggestions for future research.
The Ricardian approach (MNS 1994) is the primary method that we use in the analysis in this paper. The Ricardian model assumes that each farmer wishes to maximize income subject to the exogenous conditions of their farm. Specifically, the farmer chooses the crop and inputs for each unit of land that maximizes:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE re·pro·duce
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. ] (1)
where [pi] is net annual income, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the market price of crop i, [Q.sub.i] is a production function for crop i, [X.sub.i] is a vector of annual inputs such as seeds, 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. , and pesticides for each crop i, [L.sub.i] is a vector of labor (hired and household) for each crop i, [K.sub.i] is a vector of capital such as tractors and harvesting equipment for each crop i, C is a vector of climate variables, [IR.sub.i] is a vector of irrigation choices for each crop i, W is available water for irrigation, S is a vector of soil characteristics, [P.sub.x] is a vector of prices for the annual inputs, [P.sub.L] is a vector of prices for each type of labor, [P.sub.K] is the rental price of capital, and [P.sub.IR] is the annual cost of each type of irrigation system.
If the farmer chooses the crop that provides the highest net income and chooses each 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. input in order to maximize net income, the resulting chosen net income will be a function of just the exogenous variables:
[[pi].sup.*] = f([P.sub.q,] C, W, S, [P.sub.X], [P.sub.L], [P.sub.K], [P.sub.IR]) (2)
With perfect competition for land, free entry and exit will ensure that excess profits are driven to zero. Land rents will consequently be equal to net income per hectare (Ricardo 1817; MNS, 1994).
The Ricardian function is intended to be a locus of the most profitable crops with respect to each exogenous variable Exogenous variable
A variable whose value is determined outside the model in which it is used. Related: Endogenous variable such as temperature. The net income function does not include less profitable alternatives. It consequently does not look like the response function for any single crop but rather as a flatter function across all choices. Figure 1 depicts a theoretical set of crop specific net income functions with respect to temperature as well as the overarching o·ver·arch·ing
1. Forming an arch overhead or above: overarching branches.
2. Extending over or throughout: "I am not sure whether the missing ingredient . . . Ricardian function. For example, at cool temperatures, farmers would choose to grow wheat (Triticum Triticum
genus of cultivated cereals in the family Poaceae; grazing young green crop can contribute to hypomagnesemic tetany; includes T. aestivum (T. vulgare, wheat), T. aestivum L.). As temperatures rise, farmers would no longer want to grow wheat because it would become less profitable. They instead would shift to maize (Zea mays L.). As temperatures increase further, they might want to shift to fruit (Panicum Panicum
a genus of grasses in the family Poaceae. May contain sufficient nitrate or oxalate to cause poisoning with these substances. They are highly productive and popular annual and perennial grasses and cereal crops but many of them cause hepatogenous photosensitization miliaceum) or vegetables which are more heat tolerant. The Ricardian function, Equation (2), captures the locus of maximum profits for each temperature or precipitation level. It is estimated across crops and across inputs, revealing the net effect of changing the exogenous variable. Because farmers are assumed to make adaptations that are profitable, the method automatically captures the adaptation inherent in the market (MNS, 1994).
The Ricardian model was developed to explain the variation in land value per hectare of cropland over climate zones (MNS, 1994). In repeated studies in the United States United States, officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area. , Brazil, Sri Lanka Sri Lanka (srē läng`kə) [Sinhalese,=resplendent land], formerly Ceylon, ancient Taprobane, officially Democratic Socialist Republic of Sri Lanka, island republic (2005 est. pop. and South America, the land value per hectare of cropland has been found to be sensitive to seasonal precipitation and temperature (Mendelsohn and Dinar 1999; 2003; Seo et al. 2005; Seo and Mendelsohn 2007). Similar results have also been found for crop net revenue in India, Africa, South America, and Israel (Mendelsohn and Dinar 1999; Kurukulasuirya et al 2006; Seo and Mendelsohn 2007; Fleischer et al. 2007). Because the response 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. , 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 has been used in every Ricardian study.
There have been a number of criticisms of the Ricardian approach since it was first developed. There was initially a concern about irrigation (Cline cline, in biology, any gradual change in a particular characteristic of a population of organisms from one end of the geographical range of the population to the other. 1996; Schlenker et al 2005). This study and other analyses (Mendelsohn and Dinar 2003; Kurukulasuriya and Mendelsohn 2006; Mendelsohn and Seo 2007) address this concern by examining the differences in the response to warming between irrigated and rainfed land. A related concern is the importance of water. Some studies have controlled for water supply (Mendelsohn and Dinar 2003 and Fleisher and Mendelsohn 2007). However, water data is not available in this study. This is important since climate change may reduce (or increase) the amount of water that is available to farmers and this effect is not captured in this analysis. Given China's clear dependence on irrigation water, 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. .
There have also been concerns about the role of price changes (Quiggin and Horowitz 1999). The Ricardian model does not take into account price changes and thus 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. welfare effects. Although changes in local supply might be dramatic, prices of food crops tend to be determined by global markets. With the expansion of crop production in some parts of the world and the contraction contraction, in physics
contraction, in physics: see expansion.
contraction, in grammar
contraction, in writing: see abbreviation.
contraction - reduction in others, the changes in the price of crops from global warming is expected to be small (Reilly et al. 1994). Finally, there is a concern that the Ricardian analysis does not take into account the cost of transition (Kelly et al 2005). Although we expect transition costs to be relatively small, the Ricardian method does not measure them.
III. DATA AND MODEL SPECIFICATIONS
The climate data (monthly temperature and precipitation) were gathered 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 normals) over this time period for each month.
Because we cannot include every month in the analysis because of the high correlation from month to month, we average the monthly climate data into four seasons. Winter is the averaged of December to February, spring is the average of March to May, summer is the average of June to August, and fall is the average of September to November.
Socio-economic data come from China's National Bureau of Statistics (CNBS). The data were collected by a highly trained, professional enumeration 1. (mathematics) enumeration - A bijection with the natural numbers; a counted set.
2. (programming) enumeration - enumerated type. staff in 2001 as part of the annual, nation-wide Household Income and Expenditure Survey (HIES HIES Hyper-IgE Syndrome ). The data cover 45,700 farm households in 4365 villages, 533 counties and 31 provinces.
During the survey enumerators from CNBS collected a rich set of information at both the village and household level. The data provide us with a measure for the dependent variable, net crop revenue for each household. Net crop revenue here is the gross crop revenue (or total sales for each crop) less all expenditures for production, including expenditures on seed, fertilizer, irrigation, pesticide pesticide, biological, physical, or chemical agent used to kill plants or animals that are harmful to people; in practice, the term pesticide is often applied only to chemical agents. , machinery, plastic sheeting, hired labor and custom services. All of the output that was consumed con·sume
v. con·sumed, con·sum·ing, con·sumes
1. To take in as food; eat or drink up. See Synonyms at eat.
a. by each household was given a value based on a price of the output as if it was sold on the market. Neither family labor nor a household's rent for contracted land is counted as an expenditure. Therefore, net revenue is a measure of returns to land and family labor. Based on the total cultivated cultivated,
n in herbal medicine, used to describe plants that are commercially farmed rather than collected from the wild. land of each household, we can calculate net crop revenue per hectare.
The data set also includes a number of other household and village characteristics. These variables are important from a theoretical point of view since they can give us measures of fixed factors which belong in Ricardian regressions. Using the data, we are able to construct variables that measure the education level of members of the farm household, each family's land area, a number of 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.
1. region), each household's irrigation status (measured as the share of area that is irrigated in the village) and the ease of 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). Such variables are used as control variables in the regressions. Descriptive statistics descriptive statistics
see statistics. of the key variables are in Table 1. The Table provides key data about the entire sample as well as two important subsamples: farms that rely on irrigation and farms that do not (rainfed).
In addition to information about climate and socio-economic conditions, the characteristics of a region's soils are also important determinants of net crop revenue. 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 variables for our analysis was created by generating a variable measuring the share of cultivated area with each type of soil. These soil variables are used directly in the 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. . We also include county 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. data into the regression to control the influence of elevation on net crop revenue.
In order to proceed with our analysis of the effect of climate on agriculture, we need to match the climate data with the socio-economic data of each farmer. Although there are 752 counties with meteorological stations and 533 counties in which CNBS collected HIES data, there are only 124 counties in which there are both meteorological stations and CNBS samples.
In order to ensure that we have a relatively good match between the crop revenue (and other socio-economic) data and climate information, we restrict our sample to only those households in counties with meteorological stations. In total, this means that our final sample has 8405 households in 915 villages, in 124 counties in 28 provinces. (1)
In order to capture the expected nonlinear relationship between net revenue and climate, we specify the following model to examine the impacts of climate change on agriculture in China:
V = [b.sub.0] + [b.sub.1] T + [b.sub.2] x [T.sup.2] + [b.sub.3] x P + [b.sub.4] x [P.sup.2] + [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 (j)] [d.sub.j] x [Z.sub.j] + e (3)
where the dependent variable, V, is net crop revenue per hectare (as defined above). The variables T and P represent vectors of temperature and precipitation (four seasons). In addition, we include Z, a vector of county-, village- and household-level socio-economic and other control variables. Included in Z are our measures of soil type (Z1), elevation of the county (Z2), terrain (Z3=1 if the village is located on a plain and 0 if the village is in a mountain), the share a village's cultivated area that is irrigated (Z4), the conditions of a village's road (Z5=1 if there is a road that connects the village to the outside world and 0 if there is not) and a variable measuring the distance between the village and township township: see town. government (Z6). There are also a series of household-level variables in Z, including the average education level of the laborers in the family (Z7), a household's land area (Z8) and whether or not a household belongs to a production cooperation (Z9=1 if yes and 0 if no). The symbols [b.sub.k] and [d.sub.j] are vectors of the coefficients to be estimated, and e is an error term.
In order to assess the robustness of the model, we try a number of alternative specifications of equation 3. For example, we also try using the log of net revenue as the dependent variable. We test whether precipitation and temperature are independent by adding climate interaction terms. We break the sample between irrigated and rainfed villages and estimate separate regressions for each subsample sub·sam·ple
A sample drawn from a larger sample.
tr.v. sub·sam·pled, sub·sam·pling, sub·sam·ples
To take a subsample from (a larger sample). (Schlenker et al. 2005). As in Schlenker et al. (2005), we assume in this analysis that the choice of irrigation is exogenous.
Based on this model, the change in land value from a marginal change in temperature or precipitation evaluated at a particular vector of seasonal temperatures T or precipitation P is:
[partial derivative derivative: see calculus.
In mathematics, a fundamental concept of differential calculus representing the instantaneous rate of change of a function. ][V.sub.i]/[partial derivative]T = [b.sub.1] + 2 x [b.sub.2] x T
[partial derivative][V.sub.i]/[partial derivative]P = [b.sub.3] + 2 x [b.sub.4] x P (4)
With four seasons, one can calculate the marginal impact of each season. While seasonal effects might be of some interest, the more relevant expression for studying global warming is the overall change in annual climate. The annual marginal effect can be calculated as the sum of the seasonal marginal effects.
In general, China's climate is best described as monsoonal (Ren 2007). There are clear temperature and precipitation differences across China that vary by region and by season. The average annual temperature in China is 10.9[degrees]C (Figure 1). (2) From the south to the north, temperature declines steadily. For example, in the southern areas of China the average annual temperature is as high as 20-24[degrees]C. In the middle part of the country (in the Yangtze River Yangtze River
Chinese Chang Jiang or Ch'ang Chiang
River, China. Rising in the Tanggula Mountains in west-central China, it flows southeast before turning northeast and then generally east across south-central and east-central China to the East China Basin) the average annual temperature is 12-20[degrees]C. Further north, beginning in the Yellow River Basin and moving to the far north of the country, the average annual temperature is only 4-12[degres]C. As typical of temperate temperate /tem·per·ate/ (tem´per-at) restrained; characterized by moderation; as a temperate bacteriophage, which infects but does not lyse its host.
adj. regions, the temperature in China also differs significantly by season (Figure 2).
There are even greater seasonal and regional differences in precipitation. Average annual precipitation rates in China as a whole are near world average at about 820 mm (Figure 3). In the south, however, annual precipitation ranges from 1000 to 1500 mm. In the north, in the Huaihe River and Yellow River Basins, annual precipitation is only 600-1000 mm. It is only 500-600 mm in the rest of northern China. Generally, it is also quite dry in Western China.
The seasonal patterns of precipitation also vary by region. In the north, more than 70 percent of each year's precipitation is concentrated in the summer. Precipitation during the winter months is very low, less than 5 percent of the annual total (Ren, 2007). In contrast, in the south, precipitation is mainly concentrated in the spring as well as the summer. These regional differences in climate may be reflected in our results, where climate change has different effects on regions with different present climates.
Recent evidence indicates that global temperatures have been rising since 1750 and especially since 1950 (IPCC 2007). There is supporting evidence in China as well of temperature increases between 1950 and the present (Ren, 2007). Of much greater concern are projections that temperatures will rise even more quickly into the future (IPCC 2007). It is not yet clear how large these temperature changes will be, but climate research consistently predicts warming (IPCC 2007). The exact amount of warming across China is therefore not known, but scientists are confident warming will occur here. The climate models also all predict an increase in global precipitation but how these changes are distributed across different regions is not yet known. Individual locations across China may get more or less rainfall. The change in precipitation patterns is more uncertain than the change in temperature for China.
Relationship between Net Crop Revenue and Climate
On average, in 2001 the crop net revenue in China was 10,146 Yuan per ha (1353 USD USD
In currencies, this is the abbreviation for the U.S. Dollar.
The currency market, also known as the Foreign Exchange market, is the largest financial market in the world, with a daily average volume of over US $1 trillion. ) (Table 1). The reliance on irrigation and the availability of ample rain in certain regions of China has led to relatively high net revenues compared to other countries (even developed countries such as the US) (Rozelle et al., 2007). The high levels of per hectare output in China offset the somewhat lower real prices. These net crop revenues differ by region. In general, net crop revenue in the south is higher than in the north and net revenues are higher in the east than in the west.
Just as significantly, if not more, net crop revenues also vary between villages that are irrigated and those that are rainfed (Table 1). The average net crop revenue in irrigated villages was 12319 Yuan per hectare (1643 USD), a rate that is more than 20 percent higher than average. In contrast, average net revenues in rainfed villages were only 7464 Yuan per hectare (995 USD), more than 25 percent lower than average.
Simple statistics indicate that there is possibly some relationship between climate and net crop revenue. In Table 2, we group farms by net revenue. Farms with higher net revenues tend to have higher temperatures and more rain. For example, the twenty percent of farms with the lowest net revenues had annual temperatures of 8.2[degrees]C and annual precipitation of 595mm. In contrast, the twenty percent of farms with the highest net revenue had temperatures of 15.8[degrees]C and precipitation of 1152 mm. This positive association between net revenue, precipitation, and temperature applies to both rainfed and irrigated farms.
Table 2, of course, does not control for many factors that might vary from farm to farm. In order to do a more complete analysis, we must control for these factors. It is also important to do a more thorough job of exploring the role of seasonal variation in climate. We therefore turn to the Ricardian regressions to do a more thorough analysis of how climate and other factors affect net revenues.
Ricardian Regression Analysis In statistics, a mathematical method of modeling the relationships among three or more variables. It is used to predict the value of one variable given the values of the others. For example, a model might estimate sales based on age and gender.
In Table 3, we explore a regression model of net revenue per hectare on climate, soils, and a number of farm variables. We examine this regression for three samples: all farms, farms that are irrigated, and farms that are rainfed (no irrigation). Note that there are 8405 farms in the full sample, there are 2750 irrigated farms, and there are 2119 rainfed farms. There are approximately 3500 farms in villages with a mix of rainfed and irrigated farms where we cannot determine whether the farm is irrigated or not. The goodness of fit Goodness of fit means how well a statistical model fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e. measures (adjusted [R.sup.2)] for all of the models range from 0.17 to 0.26, a level that is relatively high for cross sectional sec·tion·al
1. Of, relating to, or characteristic of a particular district.
2. Composed of or divided into component sections.
n. household data (3).
The analysis of all farms shown in the first column in Table 3 reveals that many of the control variables are highly significant. Clay and silt soils increase net revenues per hectare (compared to sand). It is advantageous for a farmer to be on a plain, have access to a road, and participate in a production association. It is disadvantageous dis·ad·van·ta·geous
dis·advan·ta for a farm to be a larger size or higher elevation. Other factors such as whether the village has more irrigated land, laborers with lower education, or is closer to the township government do not matter (4).
Perhaps most important are the results for the climate variables. At least one climate variables is significant in every season except for fall temperature and summer precipitation. Many of the coefficients of the squared terms are significant implying that climate effects are nonlinear. However, the quadratic nature of the climate variables makes them difficult to interpret. In Table 4, we calculate the marginal impact of climate using both the linear and squared coefficients of each variable. The first column of Table 4 presents the annual marginal temperature and precipitation effects, calculated at the sample mean, for the entire sample. The results suggest that higher annual temperatures slightly reduce net revenues per hectare in China 10 USD/[[degrees]C. The overall temperature elasticity is -.09 (% change in net revenue/ % change in temperature). Consistent with earlier Ricardian analyses, the seasonal temperature effects are larger and offsetting. Higher spring temperatures are very harmful whereas warmer summer and especially winter temperatures are beneficial. Higher annual precipitation increases net revenue (15 USD/mm/mo). The overall precipitation elasticity is +0.8 (% change in net revenue/ % change in precipitation). As with the seasonal temperature effects, the seasonal precipitation effects are larger and offsetting. A wetter spring is harmful whereas a wetter winter is very beneficial.
We also examine a number of alternative specifications in the Table A-1 in the Annex an·nex
tr.v. an·nexed, an·nex·ing, an·nex·es
1. To append or attach, especially to a larger or more significant thing.
2. . We examine one model with the log of net revenue as the dependent variable. This model yields much higher R squared values. The model does a better job of explaining some observations with much higher net revenue per hectare than the sample average. However, the log model yields very similar results to the linear model explored in this paper. Another specification that we explored examines the importance of controlling for land per household. The land per household is correlated cor·re·late
v. cor·re·lat·ed, cor·re·lat·ing, cor·re·lates
1. To put or bring into causal, complementary, parallel, or reciprocal relation.
2. with climate and so whether or not it is controlled affects the climate results. However, using the log of land or using a quadratic to approximate the role of farm size has similar effects. A third important variant variant /var·i·ant/ (var´e-ant)
1. something that differs in some characteristic from the class to which it belongs.
2. exhibiting such variation.
adj. that we explored concerns adding climate interaction terms. We found that these terms were generally insignificant except for the fall season. However, adding interaction terms confounds the role of temperature and precipitation so that marginal effects depend upon both variables. For simplicity, we rely on the model presented in this paper. However, the results are robust across a number of specifications.
Because of the importance of irrigation in China, it is helpful to understand the climate sensitivity of rainfed versus irrigated farms (as first suggested by Schlenker et al. 2005). Earlier research has indicated that rainfed and irrigated farms have different climate sensitivities in Africa (Kurukulasuriya and Mendelsohn 2007) and South America (Mendelsohn and Seo 2007). We consequently split the Chinese sample between farms that were in rainfed villages and farms that were in irrigated villages. Farms that were in villages with both were omitted. We then estimated the net revenue model on the two subsamples as shown in columns 2 and 3 of Table 3.
Most of the coefficients for rainfed and irrigated farms are not similar to each other. The one exception is that larger plots for both samples have lower net revenues. Other variables, such as percent clay soil, distance to township government, share of labor that is uneducated, and farmer characteristics remain insignificant. But the irrigated and rainfed regressions often had different coefficients. Silt soil and participating in a production association increased the net revenue of irrigated land but had no significant effect on rainfed land. Being on a plain increased the value of rainfed land but decreased the value of irrigated land. Being on a road increased the value of rainfed land but had no effect on irrigated land. Higher elevation decreased the value of rainfed land but had no effect on irrigated land.
The climate coefficients for the rainfed and irrigated regressions in Table 3 were also different. Many of the climate coefficients are still significant. Some had the same size though not the same magnitude. Finally, some coefficients switched sign, such as fall temperature, summer precipitation, and fall precipitation. However, to judge the effect of climate, it is helpful to calculate the marginal impacts. The results, shown in columns 2 and 3 of Table 4 reveal that temperature has a very different effect on irrigated versus rainfed farming. Higher annual temperatures increase the net revenue of irrigated farms by +68 USD/C but reduce the net revenue of rainfed farms by -95 USD/C. The seasonal effects are also different. Warmer falls are particularly harmful to irrigated farms whereas warmer summers and winters are beneficial. In contrast, warmer springs and falls are harmful to rainfed farms whereas warmer winters are beneficial. Higher annual precipitation, however, has almost identical effects on irrigated and rainfed farms. Wetter climates increase irrigated net revenues by 27 USD/mm/mo and rainfed net revenue by 23 USD/mm/mo. Both irrigated and rainfed farms prosper more than the full sample regression suggests. The lower marginal values in the full sample may be due to a measurement error because the full cost of irrigation is not measured. As rain increases, farmers find it profitable to switch from irrigation to rainfed agriculture to save irrigation costs. In practice, they earn more. But using this data without irrigation costs, it appears that they are switching from high valued irrigation to low valued rainfed farming.
Although the average effect of temperature is negative and the marginal effect of precipitation is positive, the effects are quite different in different regions of the country. In order to understand how climate impacts vary across China, the marginal impact of temperature and rainfall for the full sample are mapped across China in Figures 4 and 5. The maps indicate what would happen with small changes in climate in the immediate future. Figure 4 on temperature suggests distinct spatial patterns with gains in the mid latitude latitude, angular distance of any point on the surface of the earth north or south of the equator. The equator is latitude 0°, and the North Pole and South Pole are latitudes 90°N and 90°S, respectively. region of China (up to 127 USD/ha/[degrees]C) but damages in the southern and northern latitudes (up to -165 USD/ha/[degrees]C). The marginal impact of precipitation is mapped for all farms in Figure 5. Additional precipitation in the wet southeast would be harmful (up to -153 USD/ha/mm/mo). Places that are already wet will lose from more rain. The rest of China would enjoy small gains (up to 65 USD/ha/mm/mo).
Maps 4 and 5 include both the effects on rainfed and irrigated farms. In order to understand what happens to each type of farm, we address them separately in the remaining figures. The marginal temperature results of the irrigation regression are shown in Figures 6. The temperature impacts in Figure 6 are not similar to those in Figure 4. With irrigated farms, warmer temperatures are more beneficial in the southeast and southwest region (128-255 USD/ha/[degrees]C). Further, irrigated farms in the far south are no longer harmed by warming. However, the rest of China has similar results. Farms in the central region continue to enjoy mild benefits from warming (up to 127 USD/ha/[degrees]C). The far north has the same marginal damages. The marginal precipitation effects for irrigated farms are shown in Figure 7. There remain some strong similarities with Figure 5 except for one major difference. The damages in the wet southeast disappear and become small benefits. All irrigated farms in China enjoy small benefits from increased rain.
The marginal temperature results of the rainfed farm regression are shown in Figure 8. The temperature impacts show a marked progression as one moves from the far south to the far north. There are large damages (-166 to -331 USD/ha/[degrees]C) in the far south from warming. These turn into smaller damages in most of the rest of the country (up to -165 USD/ha/[degrees]C). The far north and a few cold places in the southeast get small gains from warming (up to 127 USD/ha/[degrees]C). The results imply that most of China is slightly too warm for rainfed agriculture. Any further warming is therefore harmful except in the far north. The marginal precipitation effects are shown in Figure 9. Figure 9 is almost identical to Figure 5. Increased rain will damage rainfed farms in the wet southeast but benefit rainfed farms in the rest of the country.
V. CONCLUSION AND POLICY IMPLICATIONS
This study conducts a Ricardian analysis on 8405 farm households across 28 provinces in China. Net revenues are regressed on seasonal climate and a number of control variables. Several specifications of the model are estimated. The empirical results are robust. The average impact of higher temperatures is negative and the average impact of more precipitation is positive. However, marginal increases in temperature and rainfall have very different effects on different farm types in different regions. Warming is beneficial to some farmers in China but harmful to others. Rainfed farmers are more vulnerable than irrigated farmers. Warming is likely helpful to rainfed farmers in very cold places but it will likely harm rainfed farmers in most of China and especially the far south. More rain is likely to be harmful to rainfed farmers in the wet southeast but will benefit farmers in the remaining regions. Irrigated farmers are less sensitive to temperature. However, irrigated farmers, like rainfed farmers, will gain from increased rainfall.
These basic results are similar to results from other countries (MNS 1994; Mendelsohn et al 2001; Mendelsohn and Dinar 2003; Kurukulasuriya et al 2006; Seo and Mendelsohn 2007). First, climate has an effect on net revenue in every country. Second, higher temperatures increase the net revenues of irrigated farms. Third, higher temperatures are beneficial to rainfed farms in cooler climates but harmful to rainfed farms in warm or hot climates. Fourth, more precipitation is beneficial unless there is an excessive amount of rain. Fifth, seasonal impacts vary and are offsetting.
Our results, however, are not completely consistent with previous economic work on Chinese agriculture (Liu et al., 2004). Our study finds that warming is harmful to Chinese agriculture whereas Liu et al. found it was beneficial. We believe that this difference may lie in choice of data sets. We believe that the farm data set in this study is far more reliable than the county data set used by Liu et al. However, not all of the results of the two studies were different. Both studies found that increased rainfall was beneficial. Both studies found that climate effects are nonlinear and effects differ by season. Hence, although the temperature results are different, many of the results of the two studies are similar.
What about comparisons between our economic analysis and crop studies? Although both analyses predict that global warming will be harmful to China's agriculture, the economic analysis suggests that the impact will be smaller. What explains the difference between the economic results and the crop study results? We believe that the crop study models lead to more pessimistic pes·si·mism
1. A tendency to stress the negative or unfavorable or to take the gloomiest possible view: "We have seen too much defeatism, too much pessimism, too much of a negative approach" results because they do not consider adaptation. They do not include the possibility of crop switching, changes in irrigation, or other changes that farmers might undertake. These adaptations are implicitly captured in the Ricardian method.
The marginal effect of higher temperature for China is only mildly harmful for two important reasons. First, a very large fraction of farms in China are irrigated. Second, the rainfed land in China is largely in temperate or cool regions. Small amounts of warming are consequently not as harmful. Of course, some regions of China may suffer large damages. The dry Western region is vulnerable to global warming scenarios. However, the agricultural sector as a whole in China is only mildly vulnerable.
An important message in the research is that irrigation is critical to China's agriculture system. Part of China's ability to cope with future climate change depends on its capacity to use water for irrigation; nearly 60 percent of cultivated land in China is irrigated. Our analysis assumes that water will continue to be available. Data was not available to measure the amount of water each farmer was using. It was therefore not possible to measure the importance of available water. This could be a critical problem for China if climate warming makes water increasingly scarce. The negative results of this study could become much larger if warming forces many irrigated farms to become rainfed farms. Clearly there is a strong need in China for further analysis of the effects of climate change on water.
Can China continue feeding itself if climate changes? Based on our empirical results, the answer is yes, the likely gains realized by some farmers will nearly offset the losses that will occur to other farmers in China. An important caveat, however, is that our analysis assumes that there will be no change in water supply. However, it is likely that with at least some climate scenarios, water supplies will be reduced which could lead to large losses. The effect of water needs to be incorporated in future studies.
It is also quite apparent that the effects of climate change are not going to be uniform across the country. Warming will assist areas that are currently very highly productive and will further handicap handicap
In sports and games, a method of offsetting the varying abilities or characteristics of competitors in order to equalize their chances of winning. Handicapping takes many, often complicated, forms. areas that have below average productivity. In particular, warming will help the southeast region but hurt the west and far north. Chinese policy makers need to be aware that warming is likely to impose additional costs on specific regions that already have below average incomes.
The fact that the crop studies predict much larger damages than the Ricardian studies suggests that adaptation matters. The ability of Chinese farmers to change and adapt to new conditions has allowed China to outperform Outperform
An analyst recommendation meaning a stock is expected to do slightly better than the market return.
Exact definitions vary by brokerage, but in general this rating is better than neutral and worse than buy or strong buy. other agricultural economies in the world and will continue to be important with respect to climate change. However, for farmers to be able to endure future climate changes, it is critical that policies allow them to get the most out of the available factors of production and natural resources. The results of this study suggest that the direct effect of temperature rise and precipitation change on farms may not be a great risk to China in the near future. However, the effect of climate change on water is likely to be quite important. Given that water is already a very critical resource in certain regions of China,
policy makers may want to use this resource wisely, especially in regions where water is scarce. Climate change increases the pressure to develop institutions and infrastructure in water scarce regions to treat water as a valuable resource. Although uniform national policies have many desirable properties, when it comes to water, it is critical to develop efficient policies in the water scarce regions.
In order to address future warming, China may also consider developing management practices and new varieties (crops and 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. ) for a warmer world. Finally, China would benefit from adaptation at large, by having new technologies (research), educating farmers about better technologies (extension), and building credit institutions to allow farmers to purchase and apply needed technology.
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ANNEX: CAN CHINA CONTINUE FEEDING ITSELF? The Impact of Climate Change on Agriculture
Table A-1 Alternative Ricardian Regressions of All Farms With interaction terms Net crop Log net crop revenue revenue Spring temp -457.7 -0.2487 (0.63) (5.02) *** Spring temp sq -92.0 -0.00612 (3.94) *** (3.83) *** Summer temp -3,702 -0.2419 (3.39) *** (3.25) *** Summer temp sq 105.48 0.01057 (4.44) *** (6.52) *** Fall temp 2,403 0.415 (2.85) *** (7.21) *** Fall temp sq -81.05 -0.01529 (2.33) ** (6.43) *** Winter temp 1,593 0.2519 (5.23) *** (12.11) *** Winter temp sq 76.37 0.01072 (7.55) *** (15.52) *** Spring prec -325.27 -0.03730 (5.78) *** (9.71) *** Spring prec sq 1.06 0.00010 (7.50) *** (10.40) *** Summer prec -63.78 -0.00126 (1.72) * (0.49) Summer prec sq -0.11 -0.00001 (2.67) *** (2.90) ** Fall prec 20.28 0.00264 (0.39) (0.74) Fall prec sq 1.24 0.00018 (5.20) *** (11.13) *** Winter prec 538.53 0.05703 (7.10) *** (11.01) *** Winter prec sq -6.88 -0.00075 (7.23) *** (11.55) *** Spring prec*temp -0.835 0.00062 (0.28) (3.01) *** Summer prec*temp 4.08 0.00014 (2.63) *** (1.31) Fall prec*temp -8.62 -0.00172 (2.79) *** (8.18) *** Winter prec*temp 15.93 -0.00023 (2.44) ** (0.51) Share of land areas 5,477 with clay soil 0.556 (8.07) *** (12.01) *** Share of land areas 3,412 with silt soil 0.300 (6.02) *** (7.76) *** Plain (1=Yes; 0=No) 727 0.171 (2.06) ** (7.11) *** Road (1=Yes; 0=No) 2,771 0.108 (3.86) *** (2.21) ** Distance to township -32.02 government -0.001 (1.07) (0.69) Share of irrigated 17.21 areas in village 0.00362 (4.01) *** (12.36) *** If participate production 2,747 association 0.168 (1=Yes; 0=No) (3.86) *** (3.45) *** Share of labor without 0.364 -0.00073 receiving education (0.05) (1.48) Cultivated land area per household -1,992 -0.310 (11.66) *** (26.55) *** Constant 41,700 10.41 (4.05) *** (14.81) *** Observations 8405 8405 Adjusted R-squared 0.15 0.39 F-test 51.21 189.32 Without intraction terms Net crop Log net crop revenue revenue Spring temp 609.0 -0.2420 -0.92 (5.31)*** Spring temp sq -113.7 -0.00316 (5.50) *** (2.23)** Summer temp -2,121 -0.3572 (2.38) ** (5.84)*** Summer temp sq 68.99 0.01219 (3.47) *** (8.95)*** Fall temp 719.6 0.4800 (1.14) (11.07)*** Fall temp sq -5.69 -0.01911 (0.25) (12.22)*** Winter temp 1,194 0.1972 (4.18) *** (10.07)*** Winter temp sq 58.08 0.00996 (6.49) *** (16.23)*** Spring prec -304.86 -0.02262 (8.31) *** (8.99)*** Spring prec sq 1.002 0.00009 (7.79) *** (10.46)*** Summer prec 39.28 0.00460 (2.93) *** (5.01)*** Summer prec sq -0.12 -.00001 (3.10) *** (5.26)*** Fall prec -61.53 -0.02041 (1.62) (7.83)*** Fall prec sq 0.792 0.00015 (4.31) *** (11.60)*** Winter prec 469.33 0.04787 (6.56) *** (9.76)*** Winter prec sq -5.46 -0.00068 (6.56) *** (11.98)*** Spring prec*temp Summer prec*temp Fall prec*temp Winter prec*temp Share of land areas with clay soil 5,345 0.423 (8.55) *** (9.88) *** Share of land areas with silt soil 3,259 0.311 (5.87) *** (8.18) *** Plain (1=Yes; 0=No) 975 0.196 (2.83) *** (8.30) *** Road (1=Yes; 0=No) 2,584 0.103 (3.64) *** (2.11) ** Distance to township government -30.89 0.002 (1.04) (1.11) Share of irrigated areas in village 15.68 0.003 (3.68) *** (11.82) *** If participate production association 2,601 0.137 (1=Yes; 0=No) (3.67) *** (2.82) *** Share of labor without 0.517 -0.001 receiving education (0.07) (1.88) * Cultivated land area per household -1,925 -0.303 (11.35) *** (26.09) *** Constant 22,465 11.12 (2.97) *** (21.44) *** Observations 8405 8405 Adjusted R-squared 0.15 0.39 F-test 58.47 213.53 Absolute value of t statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Table A-2 Alternative Specifications of Irrigated and Rainfed Farms Net crop revenue Irrigated farm Rainfed farm Spring temp 6,811 -1,466 (2.80) *** (1.37) Spring temp sq -324.8 -76.5 (3.85) *** (1.84) * Summer temp 7,285 -8,742 (2.11) ** (5.80) *** Summer temp sq -119.55 254.25 (1.67) * (6.78) *** Fall temp -8,845 6,780 (3.35) *** (5.19) *** Fall temp sq 331.65 -258.73 (3.05) *** (4.07) *** Winter temp 2,238 1,583 (3.04) *** (2.67) *** Winter temp sq 51.61 97.91 (1.41) (6.55) *** Spring prec -294.88 -177.44 (2.41) ** (1.39) Spring prec sq -0.99 0.61 (2.47) ** (1.44) Summer prec 148.14 17.94 (1.05) (0.28) Summer prec sq -0.158 0.087 (1.41) (1.06) Fall prec -127.11 102.74 (0.73) (1.16) Fall prec sq 5.631 3.519 (5.12) *** (5.61) *** Winter prec 13.25 864.14 (0.06) (5.91) *** Winter prec sq 6.461 -14.785 (2.22) ** (7.09) *** Spring prec*temp 26.918 -3.269 (3.37) *** (0.46) Summer prec*temp 0.117 -2.701 (0.02) (1.02) Fall prec*temp -50.82 -33.10 (3.23) *** (4.16) *** Winter prec*temp -33.27 82.57 (1.90) * (4.84) *** Share of land areas with clay soil -1,934 -1,591 (1.29) (1.02) Share of land areas with silt soil 4,141 3,746 (3.58) *** (3.61) *** Plain (1=Yes; 0=No) -463 1,095 (0.56) (1.75) * Road (1=Yes; 0=No) 564 4,660 (0.42) (4.84) *** Distance to township government 72.0 -50.7 (0.98) (1.26) If participate production 3,138 -2,586 association (1=Yes; 0=No) (2.70) *** (1.46) Share of labor without receiving 32.9 -9.87 education (2.21) ** (0.92) Cultivated land area per household -2,720 -1,189 (5.78) *** (5.95) *** Constant -66240 65,090 (2.10) ** (4.47) *** Observations 2750 2119 Adjusted R-squared 0.12 0.20 F-test 14.94 20.25
"The authors would like to thank Jianming Cao, Cheng Cheng, Yumin Li and Hao hao
n. pl. hao
See Table at currency.
Noun 1. Li for their assistance in data cleaning. The paper benefited from review comments by Hui Liu, Wolfram wolfram: see tungsten. Schlenker and Apurva Sangi. We also thank the State Office of Agricultural Comprehensive Development in China (SOCAD) for their support--the writing of this paper took place in parallel with work carried in the context of the proposed Global Environmental Facility financed project "Adaptation to Climate Change" to be implemented by SOCAD. Additional support was provided by DECRG in the World Bank. The views expressed in this paper are those of the authors and should not be attributed to the World Bank."
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(1) We have tried various approaches (such as linear and non-linear regression; GIS (1) (Geographic Information System) An information system that deals with spatial information. Often called "mapping software," it links attributes and characteristics of an area to its geographic location. methods) to extrapolate extrapolate - extrapolation the climate data from the location of the meteorological stations in the counties with weather data across the landscape of counties and villages in adjacent counties (or those counties without weather data). However, our results suggest that such extrapolation (mathematics, algorithm) extrapolation - A mathematical procedure which estimates values of a function for certain desired inputs given values for known inputs.
If the desired input is outside the range of the known values this is called extrapolation, if it is inside then methods introduce substantial amounts of data measurement error into the analysis. In order to avoid such measurement error in the climate variables, we have chosen to drop all farm households that are in counties that do not have climate data (i.e., that do not belong to a county with a meteorological stations). In addition, we dropped those households which did not cultivate cul·ti·vate
tr.v. cul·ti·vat·ed, cul·ti·vat·ing, cul·ti·vates
a. To improve and prepare (land), as by plowing or fertilizing, for raising crops; till.
b. any crops (characterized char·ac·ter·ize
tr.v. character·ized, character·iz·ing, character·iz·es
1. To describe the qualities or peculiarities of: characterized the warden as ruthless.
2. with total cropping sown areas of zero).
(2) Temperature here means the Surface Air Temperature (SAT).
(3) The adjusted [R.sup.2] of our estimation results are also similar to that in other countries, for example, in the research of Africa (Kurukulasuriya and Mendelson, 2006), the adjusted [R.sup.2] is 0.35; for Brazil and India, it is 0.40 and 0.56 separately (Mendelson, et.al., 2007).
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 CCAP Center for Clean Air Policy
CCAP Cahier des Clauses Administratives Particulières
CCAP Child Care Assistance Program
CCAP Climate Change Action Plan
CCAP Culture Collection of Algae and Protozoa
CCAP Church of Central Africa Presbyterian ), Institute of Geographical Sciences and Natural Resource Research, 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 , Beijing, China (email@example.com). Robert Mendelsohn Robert Mendelsohn may refer to:
The institute is composed of five research centers:
* Corresponding author:
Center for Chinese Agricultural Policy
Chinese Academy of Sciences
Jia, No.11, Datun Rd, Anwai
Beijing 100101, China
Phone: (8610) 64889841
Fax: (8610) 64856533
Table 1 Descriptive statistics for major variables used for analyzing the determinants of net crop revenue All farm Irrigated farm Mean Standard Mean Standard deviation deviation Net cropping revenue per ha (Yuan/yr) 10146 12280 12319 12846 Spring temp ([degrees]C) 13.2 4.7 13.8 3.5 Summer temp ([degrees]C) 24.2 3.2 25.1 2.6 Fall temp ([degrees]C) 13.7 5.6 14.4 4.9 Winter temp ([degrees]C) 0.3 8.5 0.9 6.7 Spring prec (mm/month) 76.2 65.3 81.7 79.1 Summer prec (mm/month) 144.2 62.5 128.4 72.1 Fall prec (mm/month) 56.8 32.5 48.6 31.4 Winter prec (mm/month) 23.2 24.1 28.2 27.8 Share of land areas with 30 38 31 40 clay soil (%) Share of land areas with silt soil (%) 31 39 28 36 Plain (1=Yes; 0=No) 0.45 0.50 0.75 0.43 Road (1=Yes; 0=No) 0.97 0.18 0.97 0.18 Distance to township government (km) 6.1 4. 5 5.2 3.6 Share of irrigated areas in village (%) 48.9 39.9 If participate production association (1=Yes; 0.03 0.18 0.05 0.22 0=No) Share of labor without receiving education (%) 7.5 18.5 6.1 16.1 Cultivated land area per 0.72 1.00 0.57 0.72 household (ha) Rained farm Mean Standard deviation Net cropping revenue per ha (Yuan/yr) 7464 9736 Spring temp ([degrees]C) 11.05 4.7 Summer temp ([degrees]C) 22.6 3.4 Fall temp ([degrees]C) 11.1 5.6 Winter temp ([degrees]C) -3.3 8.9 Spring prec (mm/month) 53.2 43.4 Summer prec (mm/month) 139.8 51.9 Fall prec (mm/month) 53.8 33.2 Winter prec (mm/month) 15.0 19.0 Share of land areas with 17 31 clay soil (%) Share of land areas with silt soil (%) 43 43 Plain (1=Yes; 0=No) 0.35 0.48 Road (1=Yes; 0=No) 0.95 0.22 Distance to township government (km) 7.1 5.2 Share of irrigated areas in village (%) If participate production association (1=Yes; 0.01 0.11 0=No) Share of labor without receiving education (%) 9.6 21.6 Cultivated land area per 0.99 1.29 household (ha) Note: The observation for all households is 8405, the observation for irrigated households is 2750 and the observation for rainfed households is 2119. Table 2 Net crop revenue, temperature and precipitation in 2001 Grouped by net crop Average net Temperature Precipitation revenue crop revenue crop revenue Annual Annual Yuan/hectare Yuan/hectare [degree]C mm All farm 7-3339 1886 8.2 595 3340-5895 4607 11.5 798 5895-8821 7238 13.9 946 8823-13595 10875 14.8 1015 13597-184346 26125 15.8 1152 Irrigated farm 88-5399 3482 10.5 541 5402-7841 6635 13 740 7851-10456 9177 14.2 936 10484-15493 12670 14.4 946 15531-168394 29630 15.7 1141 Rainfed farm 8-2147 1226 6.9 506 2151-3966 3013 8.1 703 3973-6217 5054 10.1 789 6227-10698 8104 12.8 971 10714-173210 19952 13.9 958 Note: We sort the net crop revenue and then divide the samples into five groups where each group has the same numbers of samples. In the all farm sample, the sample number of each group is 1681. In the irrigated farm sample, the sample number of each group is 550. In the rainfed farm sample, the sample number of each group is 424. Table 3 Regressions of Net Crop Revenue Net Crop Revenue (Yuan/ha) All Farms Irrigated Rainfed Spring temp 1,453 4,149 1,789 (2.18) * (1.79) (1.54) Spring temp sq -118.1 -170.4 -106.9 (5.88) ** (2.18) * (2.97) ** Summer temp -1,803 1,263 -6,200 (2.01) * (0.57) (4.75) *** Summer temp sq 48.7 17.0 125.9 (2.53) * (0.35) (4.03) *** Fall temp 119 -5,178 2,678 (0.20) (2.55) * (2.54) * Fall temp sq -12.1 67.7 -116.1 (0.56) (0.93) (2.60) * Winter temp 1,226 2,064 911 (4.44) ** (3.64) ** (1.66) Winter temp sq 62.6 63.9 67.2 (7.34) ** (2.91) * (4.87) ** Spring prec -300.6 -268.3 -132.3 (8.52) ** (2.84)* (1.50) Spring prec sq 1.0574 0.7255 0.6050 (8.56) ** (2.21) * (1.69) Summer prec 5.61 151.1 -76.5 (0.39) (3.68) ** (2.70) * Summer prec sq -0.06078 -0.2414 0.1322 (1.55) (2.22) * (1.64) Fall prec -107.4 -413.8 -171.6 (2.92) * (3.67) ** (2.71) * Fall prec sq 0.9442 2.3112 1.2763 (5.31) ** (3.22) ** (4.25) ** Winter prec 554.4 668.9 655.9 (8.07) ** (3.43) ** (5.33) ** Winter prec sq -6.355 -5.212 -8.248 (7.96) ** (2.42) * (5.27) ** Share of clay soil 4,360 201 -109 (7.26) ** (0.14) (0.08) Share of silt soil 2,080 2,865 747 (3.85) ** (2.68) ** (0.79) Plain (1=Yes; 0=No) 856 -1,459 1,248 (2.57) * (1.96) * (2.11) * Road (1=Yes; 0=No) 2,022 722 3,313 (2.96) ** (0.55) (3.66) ** Distance to township 21.9 83.4 -35.8 government (0.77) (1.19) (0.93) Share of irrigation in 4.6 village (1.11) If participate production 1,713 2,940.6 -2,168.4 association (1=Yes; 0=No) (2.50) * (2.57) * (1.27) Share of labor without 4.901 24.6 -9.3 education (0.71) (1.71) (0.90) Log of cultivated land area -5,189 -4,942 -3,934 per household (29.46) ** (13.72) ** (14.53) ** Elevation -1.956 -0.920 -3.493 (4.56) ** (1.41) (2.46) * Constant 26,242 -4,167 70,431 (3.28) ** (0.19) (5.22) ** Observations 8405 2750 2119 Adjusted R-squared 0.21 0.17 0.26 F-test 89.23 * denotes significant at 5%, ** denotes significant at 1% level Table 4 Marginal impacts of climate on crop net revenue All Irrigated Rainfed farm (a) farm (b) farm (c) Temperature (USD/ha/[degrees]C) Spring -230 * -49 -143 ** Summer 76 * 286 -15 ** Fall -29 -458 ** -68 * Winter 173 ** 288 ** 130 Annual -10 * 68 * -95 * Annual Elasticity -0.09 * 0.62 * -0.88 * Precipitation (USD/ha/mm/mo) Spring -19 ** -22 ** -6 Summer -2 11 ** -5 * Fall -1 -21 ** -4 * Winter 36 ** 59 ** 38 ** Annual 15 * 27 ** 23 * Annual Elasticity 0.80 * 1.48 ** 1.24 * * denotes significant at 5%, ** denotes significant at 1% level Yuan converted to 2006 USD using exchange rate of 8 Yuan/USD. We wanted to allow easy comparison of marginal impacts with studies in other countries.