In this study, I demonstrated the use of the DSSAT model in investigating the nature of the shift in supply when a hybrid rice variety was used. The method that I have presented here is the first of its kind. Though far from perfect, this study has demonstrated the use of
107 the DSSAT model in examining the effect of adopting a new technology on input demand and output supply. The use of this crop simulation model has enriched the economic analysis by considering in detail just how the change in technology affects the supply curve, rather than treating this process as a black box.
Aside from applications in assessing the economic impacts of new agricultural technology, the method presented can also be applied to evaluating the environmental effects of adopting a technology. Evaluations of the environmental impacts of
over-fertilization, nitrogen loss to denitrification, and methane emission from rice are only some of the potential applications of the DSSAT model. The use of this model can also encourage greater collaboration between various disciplines, such as agricultural sciences and
economics, leading to more holistic policy recommendations.
I consider the use of crop models as an approach complementary to econometric analysis. Ordinarily, the use of survey data in estimating a production function leads to an endogeneity bias in the estimated coefficients because of the correlation of input variables to unobserved variables such as technology, weather, soil, and management. The use of DSSAT circumvents this problem because it generates yield data under the same technology, weather, soil, and management variables. This makes the use of OLS in estimating yield response functions feasible even without panel data. In turn, this enables an analyst to isolate and econometrically investigate the true relationship between the output and variable inputs without worrying about endogeneity. Additionally, if the crop model is calibrated well, especially for extreme amounts of inputs, the parts of the production function and the individual supply curve can be examined closely without relying on the
108 observed levels of input use. Given this, the supply function can be extrapolated back to the price axis with greater confidence than when using survey data and econometric methods alone.
While the presented method can be useful, there are major issues and challenges that need to be addressed to optimize the use of the DSSAT model as a complementary analytical tool in assessing the welfare effects of a new agricultural technology. First, the DSSAT model requires huge amounts of data to run simulations. Adequate data on weather conditions, soil properties, existing crop management practices, and plant characteristics may not be available for many desired studies. Fortunately, this problem can be addressed by improving and standardizing data collection and database management for different experiment stations of research organizations (i.e. universities, public research institutes, private research organizations). For example, the NCT-MAT project could be used as a platform to increase availability of data for testing more hybrid and inbred varieties in various production environments in the Philippines. Through this, a better way of examining the effect of adopting hybrid rice varieties on the industry supply curve may be possible.
The second issue centers on the calibration process, which affects how well the DSSAT model predicts real production at extremely low and high levels of input application.
This study has shown the pivoting of the hybrid and inbred yield response functions at combinations of very high water level and low nitrogen applications, which could be a reflection of a poorly calibrated model. It is also interesting to note that the model finds potassium as an insignificant input from the economic point of view, though this nutrient is known to have an important role in production. In fact, there is a significant reduction in the
109 output level when a zero potassium application is assumed compared to the scenario where it is assumed to be non-limiting. This calibration issue can be investigated further with the availability of data from experiments that use extremely low and high input applications.
The third issue is the use of a parametric representation of the yield responses. The magnitude of the welfare changes is not only affected by the nature of the shift in supply but also by the assumed functional form. In this study, the choice of the quadratic functional form partly drives the resulting behavior of the derived supply functions. In the future, it may be useful to explore nonparametric techniques to identify the individual supply curve directly from the DSSAT-generated yield data.
This study confirms that hybrid rice technology can generate a greater economic surplus for the society though it cannot fully answer whether the generated benefits could outweigh the costs of R&D of hybrid rice varieties, including the associated cost spent by the government in promoting it. However, the method that I have presented here provides a step towards a better measurement of benefits from adopting hybrid rice technology, and consequently to the measurement of returns to hybrid rice R&D.
110 4.5. Figures and Tables
P
Figure 4.1. Nature of supply shifts Panel 1: Perfectly inelastic demand
Supply Shift C.2. Parallel Supply Shift
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Temperature (o C)
Monthly Average Maximum Temperature (oC)
2005 2006 2007
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Solar Radiation (MJ/m2/day)
Monthly Average Solar Radiation (MJ/m2/day)
2005 2006 2007
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Temperature (oC)
Monthly Average Minimum Temperature (oC)
2005 2006 2007
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rainfall (mm)
Monthly Average Rainfall (mm)
2005 2006 2007
Figure 4.2. Monthly average temperature, rainfall and solar radiation at PhilRice Station, Science City of Munoz, Nueva Ecija , Philippines, 2005-2007
Source: Agronomy, Soils, and Plant Physiology Division, PhilRice, Science City of Munoz, Nueva Ecija, Philippines
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0 50 100 150 200
kg Nitrogen ha 0
2000 4000 6000 8000 10 000
kg Yield ha