The large changes in the rice markets may partially explain the fact that the gap between the yield frontier and adopted yield potential has grown by two to three times that for either wheat or maize.One of most impressive accomplishments of China’s research system is that it has been able to consistently create and deliver to the field varieties demanded by farmers, inducing them to constantly upgrade their seed stock.Our data shows that Chinese farmers adopt new varieties with great regularity.6 For example, maize farmers turn their varieties over the fastest, averaging more than 33 percent per year.Every 3 years farmers on average replace all of the varieties in their fields.In the case of rice, farmers replace all of the varieties in their field every 4 years and wheat farmers adopt varieties at the slowest rate, changing their varieties every 5 years.From conversations with those familiar with grain cultivation in the US, Mexico, and India, as national averages, the turnover rates rival those found in the rice bowls and wheat baskets of the developing and developed world.China’s domestic research system has produced most of the new technology.The rise of the stock of research in the early reform era mostly reflects the commitment of the leadership during the Mao era.In our analysis, however, we only want to include that part of the research stock that is used to produce new varieties.To make the adjustment to our research investment series to make it include only crop research, we note that according to the Ministry of Agriculture Statistics , since at least 1980 , research administrators have consistently invested between 69 and 71 percent of its annual research budget to crop research.Of this, most of the crop research budget goes for plant breeding and closely related research projects.Therefore, in the creation of our research stock figure, we multiply the total annual research expenditure by the proportion of the budget that is allocated to crop research and apply the procedure used in Pardey et al., to create our measure of crop research stock.7 The resulting series trend up sharply through the 1980s and the early 1990s until the rising trend decelerates in the mid- 1990s, reflecting slowing rates of research investment in the 1980s.

Once the new technology has been created, China’s agricultural leaders have extended new varieties to the farmer through the national extension system.In the counties,strawberry gutter system extension agents work with village officials and farmers to get them to adopt new products.We measure extension effort by the amount of funding dedicated by the government to support such work.Researchers differ in their view about the record of performance of the government in their investment in research and extension in recent years and the implication of the trends for the state of China’s research system.Adjusting the data as suggested by Rozelle, Pray, and Huang, research investment falls or is stagnant from 1985 to early 1990.8 In the early 1990s, investment levels rise at a slow pace, until 1995 when they move up sharply.Extension expenditure trends follow a similar pattern.Slowing investment trends for long stretches of time during the 1980s, given research lags, would most likely start to show up as stagnating research stock in the mid- to late 1990s.China also has access to genetic materials from international sources for all the three crops.Especially for rice, China has drawn heavily on the international research system for genetic material.For example, material from the International Rice Research Institute comprises a large share of China’s rice germplasm.Nationwide, we can trace around 20 percent of the germplasm to IRRI varieties.The proportion varies over time and also varies by province, reaching more than 40 percent in Hunan Province, one of China’s largest rice growing provinces, in the late 1980s.Although the national use of wheat and maize materials from the CG system is lower , there does exist great variability among provinces, and in some provinces material from the CG system makes up around half of the germplasm.10 The new varieties and germplasm material, once they are introduced into the country, are used by breeders in China’s NARS and then extended through the domestic extension system.In summary, China’s research system has created large amounts of new technology and it has succeeded in getting farmers to adopt it at an impressively rapid pace.The technology appears to embody significant levels of yield-increasing material that may prove to be an important determinant of productivity.

The rate of adoption of the highest yielding material, however, is somewhat slower than the rise in yields; yields and output have grown in the past, at least in part due to increased use of inputs.If future yield increases from higher input levels are limited by already high levels of input use, future growth in yields will more increasingly rely on rise in TFP, which most likely needs to be driven by new technology.A potential statistical issue arises, however, when VT is used as a measure to test the effect of technology on TFP, as in equation.Since the farmer may be simultaneously making decisions affecting both TFP and technology adoption, an OLS regression of TFP on VT likely is problematic because the error term may be correlated with VT.To avoid the endogeneity of VT in the estimation of the TFP equation, we take an instrumental variable approach.Using predictions from an equation explaining technology as an instrument , our identification strategy assumes that the varieties created by national and international research institutes affect technology, but do not affect TFP except through the seeds farmers adopt.If the assumptions are valid, we can use three variables as instruments: the investments made by the government in crop breeding research ; germplasm flowing into each province from international agricultural research centers ;11 and, yield-enhancing germplasm from China’s NARS.To specify a technology adoption equation, we turn to Feder and Umali’s review of the agricultural innovation adoption literature for guidance.Their article shows that a large number of factors affect adoption.The size of the technology set – that is the range of choices of new technology that farmers have when they are making planting decisions – is one of the most important determinants.In addition, researchers have found that the quality of information about available technology is also necessary.In particular, a good extension system provides information to the agricultural community about available new technology while farmer learning and human capital facilitate its adoption.Both the physical environment and infrastructure also affect adoption.In areas with better natural climate and improved infrastructure , farmers were found to adopt new varieties more rapidly.Finally, the completeness of markets facilitates technology adoption, as does the existence of other local institutions that support the search for and adoption of new technology.Hence, a close reading of Feder and Umali suggests a model of technology adoption should include measures of the availability of new technology, the extension system, the nature of the physical environment, infrastructure, and market environment, and, if possible, measures of human capital.In both their role of creating instruments for the TFP equations and as equations of interest in their own right, the technology equations perform well.The R-squares in OLS versions of the technology equations exceed 0.90 for all three crops.Hausman tests for exclusion restrictions that are designed to test the validity of the instruments show our three instruments are statistically valid.13 Substantively, the first-stage equations provide interesting insights on the process of the technology creation in China.The positive and highly significant sign on the Research Stock variable in all of the specifications for all crops demonstrate the effectiveness of investments in the research system.Higher levels of national stocks accelerate the pace of varietal turnover.

If technology is the engine driving China’s food supply in the future , the results here emphasize the necessity of maintaining the level and growth of public investment in crop research and development.The negative sign on the market liberalization period dummy variable in all but one of the first stage equations calls for heightened attention to the health of the research system.The factors that have slowed technological change in the 1990s appear to be the source of fall of TFP in the 1994 and 1995.However, this may be too strong of a conclusion; the negative sign may only be picking up the fact that this just happens to be a period when China’s agricultural TFP growth is temporarily stagnant,grow strawberry in containers a phenomenon that periodically occurs in every country.For example, even in the U.S.where researchers have documented the fact that TFP has grown steadily during the entire post-WWII period , there have been at least two 5-year time periods in which TFP growth has been near zero or negative and two more that the growth rate of TFP has been only one percent, less than the rate of growth of the U.S.population.The impact of the yield-increasing technology is more complicated.Breakthroughs in higher yields lead to faster spread and replacement of new varieties for some crops but not others.The positive and significant signs of the Yield Frontier variables in the wheat VT equations demonstrate that when higher yielding wheat varieties appear in their provinces farmers turn their varieties over more frequently.The correlation between a higher yield frontier and more rapid turnover may explain why wheat yields outperformed other major grains during the reform period.In contrast, higher values of Yield Frontier variables in the rice and one of the maize equations are associated with slower turnover.Such a finding is consistent with our gap analysis and may reflect the fact farmers in the mid- to late-reform period prefer adopting higher quality rice varieties, even though higher yielding varieties are available.The Impact of CG Material The impact of the materials from the CG system is mainly a story of the China’s breeders using IRRI and CIMMYT varieties for the yield enhancement of their seed stock.If it can be assumed that, when China’s breeder incorporate foreign germplasm into its varieties, the material contributes to part of the rise in productivity, then the test of the direct impact of CG material is seen in the results of the TFP equation.

If technology is important in all the TFP equations, by virtue of the fact that IRRI’s material is used more frequently by China’s rice breeders, compared to that used by wheat and maize breeders, it is making the largest contribution of the CG system to China’s TFP in the reform era.It is possible, however, that foreign material may be bringing in an extra “boost” of productivity, beyond its contribution to the varieties themselves, by increasing the rate of turnover of new varieties.14 Such an effect would show up in the VT equations.If the coefficients of the CG variables were positive and significant, they would indicate that the presence of material from CG centers makes the varieties more attractive to farmers and contribute to technological change in China in a second way.In fact, there is not particularly strong evidence that increases in the presence of IRRI material is important in increasing the turnover of rice varieties.If farmers are, in fact, mainly looking for characteristics that are not associated with higher yields, it could be that IRRI material is making its primary impact on yields and only a secondary impact on the other traits that have been more important in inducing adoption in the reform period.A similar cautious interpretation is called for in the case of wheat and maize where the standard errors are large relative to the size of the coefficient in all but one case.But although the contribution of CIMMYT wheat and maize germplasm to China, according to this analysis, may be smaller, in some provinces the contribution of CIMMYT’s material has been large and may have extraordinary effects on the productivity of some of China’s poorest areas.For example, the CG genetic materials contributes more than 50 percent of Yunnan Province’s wheat varieties and more than 40 percent of Guangxi Province’s maize varieties in the late 1980s and early 1990s.Yunnan and Guangxi Provinces are both very poor provinces and some of the poorest populations in China are in the mountainous maize growing areas.Elsewhere , we have shown that the impact of CG material in poor provinces, in general, is more important than its effect in rich areas—both directly and in some cases in terms of inducing more rapid turnover.