Local sustainable yield and embodied resources in ecological footprint analysis—a case study on the required paddy field in Taiwan

Ecological Economics 53 (2005) 415 – 430 www.elsevier.com/locate/ecolecon

ANALYSIS

Local sustainable yield and embodied resources in ecological footprint analysis—a case study on the required paddy field in Taiwan Jiun-Jiun Ferng* Department of Real Estate and Built Environment, National Taipei University, Taipei, Taiwan 10433, ROC Received 4 November 2003; received in revised form 7 November 2004; accepted 19 November 2004 Available online 1 February 2005

Abstract Since the 1990s, ecological footprint (EF) analysis has been employed to discuss two important dimensions of sustainable development, intra- and inter-generational equity, from the perspective of ecosystem appropriation. When examining the equality of resource use among generations in the EF analysis, sustainable yield plays a crucial role; however, its value is assumed to be the same as that of average industrial yield, which does not reflect differential productivity and its value can be much higher than the sustainable one through over-exploitation practices. The estimated EF, as well as ecological deficits, would be underestimated, from which false policy implications could be drawn. Through a review of the literature on the relationships between yield, soil quality, and farming practices, this paper suggests adopting a yield potential ranking system that was established by the government in Taiwan in 1991 through an 8-year field study. This ranking system served as a proxy for local sustainable yield in estimating the required paddy fields in Taiwan in 1996 under two scenarios. Scenario (I) concerned the required paddy fields for supporting the direct and indirect consumption of rice by Taiwan residents; Scenario (II) explored the additional area of paddy fields that would be needed when the food energy from cereals is provided exclusively by rice and the importing of rice is not possible. The results were then compared with the estimates when the average industrial yield of 1996 was used. A hybrid-units input–output modeling method was used to estimate the rice contained in manufactured products. The results of the scenario analysis shed light on the importance of preserving Taiwan’s existing paddy fields for the sake of maintaining stable long-term food supply. D 2005 Elsevier B.V. All rights reserved. Keywords: Ecological footprint; Embodied resources; Hybrid-units input–output model; Local sustainable yields; Paddy field

1. Introduction

* Tel.: +886 2 25009543; fax: +886 2 25074266. E-mail address: [email protected]. 0921-8009/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2004.11.010

In the early 1990s, Wackernagel and Rees proposed measuring humanity’s reliance on ecosystem services in terms of area and coined the term

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decological footprintT (hereafter EF) (Wackernagel and Rees, 1996). Compared with carrying capacity, an important concept in environmental planning and management since the 1970s, the EF analysis is more appropriate in demonstrating humanity’s appropriation of ecosystems at the regional and national scales, because it considers international trade, through which a defined region/nation can import what they require from the rest of the world (Rees and Wackernagel, 1996). Therefore, the EF analysis helps focus attention on the often-ignored consequences of longdistance ecosystem appropriation (Warren-Rhodes and Koenig, 2001). Moreover, the EF analysis reveals the dindirectT reliance of humans on ecosystem services (Herendeen, 2000), thus suggesting a more comprehensive examination on their appropriation of an ecosystem. Less than a decade since its first appearance in the literature, the EF analysis has given rise to a wide range of discussion; for example, a forum on this subject was given in volume 29 of Ecological Economics in 2000. In addition to refining the methodology and extending the application of the EF analysis (a more detailed discussion is given in Section 2), researchers also have critically evaluated its strengths and weaknesses. For example, Herendeen (2000) considered the EF as a vivid indicator of dependence and indirect effects. Bicknell et al. (1998), Wackernagel and Silverstein (2000), and Haberl et al. (2001) appreciated its attempts to quantify the required physical throughput for supporting a defined population and to show a possible overshoot. These measurements are in line with Daly’s dscaleT (Templet, 2000), a major concern in ecological economics. Regarding the weaknesses of the EF analysis, van den Bergh and Verbruggen (1999) made a number of critical arguments against EF as an indicator of sustainable development. Some of the criticisms may stem from their unawareness of the essence of EF; that is, van den Bergh and Verbruggen failed to recognize that different research questions or purposes lead to different types of EF applications. Some of the criticisms are accepted by other researchers, including Wackernagel himself and coworkers/followers, such as querying the assumption of current yields as being sustainable (Wackernagel and Yount, 1998, 2000; Haberl et al., 2001), and disapproving of the aggregation of individual foot-

print components (van Vuuren and Smeets, 2001; Ferguson, 2001). In addition to the aggregated presentation of EFs and assumption of sustainable yield, another major issue with EF analysis concerns the estimation of resources and wastes that are required and generated in the production of goods and services (Wackernagel and Yount, 1998, 2000; Wackernagel et al., 1999a; Ferng, 2001).1 In this regard, Wackernagel et al. (1999a) suggested calculating the ratio of raw material to product; however, this would require great effort in accounting for the numerous, ever-changing products and their recipes. Another method for estimating the embodied resources in a more systematic way is input–output analysis. If a physical input–output table with sectors in fine category is available, then detailed physical-conversion relationships between products and their material inputs can be found in the Leontief inverse matrix. However, most nations that regularly produce monetary input–output tables usually have no physical input–output tables, except for a few European countries (Hubacek and Giljum, 2003).2 In the case when only a few raw materials are of concern, the lack of physical input–output tables may not hinder the estimate of embodied resources through input–output analysis. An illustration is an estimate of the energy cost of products, or embodied energy, using a hybrid-units input–output table, in which the inputs of primary energy are measured by physical units instead of a monetary one (Bullard and Herendeen, 1975; Herendeen, 1978; Miller and Blair, 1985; Battjes et al., 1998; Machado et al., 2001). In estimating the required area of paddy fields in Taiwan to support residents’ demand on rice, a type of disaggregated EF analysis, this paper attempts to deal with the query on assuming current industrial yield as sustainable, and to estimate the rice embodied in manufactured products. After relaxing the assumption 1 The standardized EF calculation uses dapparent consumption,T domestic production plus imports minus exports for each raw material of concern, as a proxy for residential consumption, without correction for the raw materials that are contained in the imported/ exported manufactured products. 2 Since the 1990s, some European countries have issued physical input–output tables; but no standardized format has been developed, mainly differing in the disaggregation of sectors and products, the choice of material to be included, and the balance condition (Hubacek and Giljum, 2003).

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that current industrial yield is sustainable in the standardized EF calculation, this paper reviews the literature on the relationships between yield, management practice, and soil quality. The reviewed literature suggests that the yield potential of a crop could be maintained under normal climatic situations and recommended farming practices. According to the argument, this paper suggests adopting a yield potential ranking system of paddy rice as a proxy for its local sustainable yield.3 Regarding the amount of rice embodied in manufactured products, this paper suggests calculating rice multipliers with a hybridunits input–output table, in which a physical unit replaces the monetary unit in measuring the consumption of rice. The remaining sections are organized as follows. Section 2 reviews the literature on the EF analysis, including the progress in improving and extending EF applications. Section 3 explores the relationships between yield, soil quality, and agricultural management practices, and describes the yield potential ranking system of paddy rice in Taiwan. Section 4 describes the methodology of the hybrid-units input– output model for estimating the resources embodied in manufactured products. Section 5 conducts a case study and discusses the results. A conclusion is given in Section 6.

2. Review of ecological footprint analysis 2.1. Definition, research focus, and issues Rees (1996: 205) defined the EF as: bThe corresponding area of productive land and aquatic ecosystems required to produce the resources used, and to assimilate the waste produced, by a defined population at a specified material standard of living, wherever on Earth that land may be located.Q According to this definition, EF only serves as an accounting tool for gauging humanity’s reliance on ecosystem services in terms of area (Rees and

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Wackernagel, 1996; Wackernagel, 1999; Ferguson, 2002). By combining with the notions of dfair earthshareT and dbiocapacity,T EF analysis can be further employed to examine whether ecosystem goods and services are consumed equally among nations in terms of per capita (Wackernagel and Rees, 1996; Ferguson, 1999; Wackernagel et al., 1999a,b) and exploited in unsustainable way (Wackernagel and Rees, 1996; van Vuuren and Smeets, 2000; Andersson and Lindroth, 2001; Haberl et al., 2001).4 Although EF analysis can highlight the issues of intra- and inter-generational equity, the two important dimensions of sustainable development, its ability as an indicator of sustainability has not been widely accepted (van den Bergh and Verbruggen, 1999; Opschoor, 2000; van Kooten and Bulte, 2000; Lenzen and Murray, 2001). Among the arguments against EF as an indicator of sustainability, space scale and conversion factor are two major concerns. Wackernagel and Rees (1996) suggest judging whether unsustainability occurs according to the presence of ecological deficits, a situation when the consumption EF of residents in a defined area exceeds the biocapacity within the boundaries. Ecological deficits occurring systematically at the global scale may imply unsustainable development (Wackernagel and Yount, 1998; van Vuuren and Smeets, 2000; Andersson and Lindroth, 2001), whereas at lower space scales, such as regions and nations, the same situation only shows the extent that a defined human society relies on the ecosystems outside the borders (Costanza, 2000; van Vuuren and Smeets, 2000, 2001). To judge whether natural capital is eroding at a national scale, further information is required, for example, the exploitation of domestic ecosystem goods and services for producing exports (Andersson and Lindroth, 2001; Lenzen and Murray, 2001; Haberl et al., 2001). In the EF calculation, consumed resources and emitted wastes are converted into areas of land and water that can continuously generate resources and assimilate wastes. The source- and sink-area conversion factors are currently estimated in different ways. The values of sink-area conversion factor are derived

3

Taiwan’s government established this system according to an 8year field research study on the relationship between the yield of paddy rice and local agricultural environment (climate, soil quality, and limiting factors). A more detailed description on this system is given in Section 3.2.

4 The EF analysis is deemed better at detecting an unsustainable path than justifying a sustainable one, because sustainable development cannot be judged definitively given current knowledge (Haberl et al., 2001).

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according to ecosystems’ assimilation capacity, for example, the amount of CO2 sequestered by forests. The estimated EF can thus reveal whether ecosystems’ assimilation capacity is overloaded, which ultimately leads to unsustainable development.5 The source-area conversion factor takes the values of current yields, assuming that the current yield is sustainable, which is, however, often not true (Rees and Wackernagel, 1996; Wackernagel and Rees, 1996; Wackernagel and Yount, 1998, 2000; Hansson and Wackernagel, 1999; Haberl et al., 2001). Moreover, at the global scale, this calculation method ensures the estimated total area required for generating the concerned resource equal to the total cultivated area for that resource, thus revealing nothing on resource over-exploitation. These two different practices have confused researchers in the way that they fail to recognize which practice can reveal unsustainable path. For example, van den Bergh and Verbruggen (1999) pointed out that the estimated energy footprint, according to the sequestration capacity of a forest, is often about half of the total EF, and thus they asserted that the sink EF is hypothetical land, and suggested that better sequestration technology and renewable energy carriers should be considered in estimating EF. However, van den Bergh and Verbruggen ignored the fact that what were calculated in those EF applications that they criticized are the amounts of CO2 already emitted into the air from fossil-fuel combustion, and failed to distinguish the difference between dsnapshotT and dwhat-ifT analysis, both of which can be adopted in the EF analysis. In a snapshot analysis as those studies criticized by van den Bergh and Verbruggen, the estimated energy footprint is the required forest area for sequestering the anthropogenic CO2 emissions in the air already (Haberl et al., 2001). An important piece of evidence for justifying this excessive demand for CO2 sequestration is the overwhelming greenhouse effect facing us today. Important information revealed from the adoption of different practices in estimating the source- and

5

This point is shared by Bicknell et al. (1998: 153); in their words, bEnergy land represents the only attempt to incorporate unsustainable practices into ecological footprint.Q

sink-area conversion factors is that the standardized EF analysis does not distinguish between sustainable and unsustainable practices of resource exploitation (Rees and Wackernagel, 1996; Bicknell et al., 1998; Wackernagel and Yount, 1998; Hansson and Wackernagel, 1999; Ferguson, 1999; van den Bergh and Verbruggen, 1999; Wackernagel et al., 1999a; Herendeen, 2000; van Kooten and Bulte, 2000; van Vuuren and Smeets, 2000; Wackernagel and Yount, 2000; Jansson and Nohrstedt, 2001; Lenzen and Murray, 2001; Haberl et al., 2001). However, lacking of such distinction in standardized EF calculations does not mean that the EF analysis intends to ignore the differences or overlooks the importance of such distinction. The point is that the required data are not easily obtained and this is the difficulty faced by all research on sustainable development (Ferguson, 1999, 2001). To what extent the lack of such distinction affects the results of EF applications depends mainly on the purposes of the research. For example, the results are not affected when one attempts to evaluate whether ecosystem goods and services are currently shared equally per capita among nations, because in such a case the consumed resources and emitted wastes are converted into area according to the world average values of industrial yield and assimilation capacity (Ferguson, 1999, 2001, 2002; van Vuuren and Smeets, 2000; Wackernagel and Yount, 2000). However, EF will be underestimated (Rees and Wackernagel, 1996; Wackernagel et al., 1999a; Wackernagel and Yount, 2000; Jansson and Nohrstedt, 2001) and the biocapacity overestimated (Hansson and Wackernagel, 1999) when one uses the level of industrial yield that is feasible only through over-exploitation practices. In this situation, a false policy on resource use could be formulated. In the case of crop production, heavy applications of chemical fertilizers and pesticides usually increase yield at the expense of soil degradation and environmental pollution, which in turn, with time lags, undermine the production potential of soil (Johnson, 1984; Matson et al., 1997). Accordingly, the EF calculated from the yield that is realized through over-exploitation practices will underestimate the demand of arable land, thus pointing little attention to the tragedy that could occur due to a continuous overtax of soil productivity.

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2.2. Progress in improving and extending EF analysis In the 1990s, the EF analysis was mainly applied to estimating (also comparing) the appropriated ecosystem service in support of residents’ consumption of different countries, and to reveal the overshoot of source- and sink-capacity at the global, regional, and local scales (Wackernagel and Rees, 1996; Wackernagel and Yount, 1998; Wackernagel et al., 1999a,b). The calculation method used in these studies uses primarily the formula proposed by Wackernagel and Rees in their book dOur Ecological Footprint,T but with some refinements (Wackernagel and Yount, 1998; Wackernagel et al., 1999a,b), such as introducing equivalence factor’ to aggregate different land categories. Not all of these refinements receive appreciation. For example, in contrast to aggregating different land categories through an equivalence factor, several researchers suggested presenting the results of the EF analysis in a disaggregated form (van den Bergh and Verbruggen, 1999; Templet, 2000; van Vuuren and Smeets, 2000; Ferguson, 2001). In addition, the suggested calculation of the ratio of raw material to product is time-consuming because it requires great efforts to obtain the data on a wide variety of products and their recipes. Except for the improvements for the standardized EF calculation, different methodologies have been proposed and applied to case studies. For example, a component-based mode of EF analysis (Simmons et al., 2000) was applied to eco-tourism evaluation (Go¨ssling et al., 2002; Hunter, 2002), freshwater requirement for humans and ecosystems (Jansson et al., 1999; Jansson and Nohrstedt, 2001), and energy planning (Sto¨glehner, 2003). In addition, input–output analysis has been an alternative method for the EF analysis since Bicknell et al. (1998) used land multiplier to estimate New Zealand’s EF. However, their calculation procedure would cause errors when distinction among land use has to be made, for example, when one uses equivalence factor to weigh the biocapacity of different land use, or when one presents the estimated EF by land use category. Thus, using the components of sectoral land multiplier instead of land multiplier itself was suggested by Ferng (2001) to avoid miscalculation in such situations. Instead of conventional monetary input–output tables, Hubacek and Giljum

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(2003) used a biophysical input–output table in estimating the EF associated with international trade. Although biophysical input–output tables allow a more accurate and systematic exploration into the physical relationship between input and product, they are only available in a few European countries (Hubacek and Giljum, 2003). For more information on expanding and enriching the EF applications by using input–output analysis, one may refer to the studies of Proops et al. (1999), Andersson and Lindroth (2001), Lenzen and Murray (2001), and Ferng (2002) as examples.

3. A proxy for local sustainable yield 3.1. Yield, soil quality, and management practices Sustainable yield is difficult to obtain definitively, because agricultural production is affected by climate, soil quality, and management practices in an interwoven way. Management practices affect crop yield directly through, for example, water/nutrient supplement and pest control (Hegde, 1996; Matson et al., 1997), and indirectly through their influences on soil quality (Reganold et al., 1987; Harris, 1996; Hegde, 1996; Matson et al., 1997; Glover et al., 2000; Bulluck et al., 2002; Doran, 2002; Wang and Yang, 2003). For example, organic farming practices help to increase soil organic matter and thus decrease soil erosion (Reganold et al., 1987; Wang and Yang, 2003). Soil quality is inherently determined by natural factors such as climate and geography and it can also be altered by humanity through farming practices (Doran and Zeiss, 2000). For example, frequent tillage operations increase the loss of topsoil, which in turn decreases organic matter and available water retention (Reganold et al., 1987). In addition, the influence of human activity on climate has emerged in recent decades (e.g., greenhouse effect), which can change the growth environment of crops and thus affect their production. Accordingly, one cannot definitively determine the sustainable crop yield for an infinite time-span, because the influencing factors themselves change with time and thus their interactions are even more difficult to predict. In the foreseeable future, management practice can be taken as a dominant factor that affects crop yield,

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marginal returns to fertilizers is stagnant, and even declining (Harris, 1996; Ko et al., 1998; Tong et al., 2003). The decreasing yield per unit of chemical fertilizer may result from the negative impacts of chemical fertilizers on soil quality (Tong et al., 2003). Several studies have shown that continuous applications of chemical fertilizers have deleterious effects on the chemical, physical, and biological aspects of soil quality that are important to crop growth (Reganold et al., 1987; Harris, 1996; Hegde, 1996; Matson et al., 1997; Glover et al., 2000; Wang and Yang, 2003). However, organic fertilizers, also blended organic– chemical fertilizers, can maintain and even improve these soil attributes (Reganold et al., 1987; Hegde, 1996; Matson et al., 1997; Glover et al., 2000; Bulluck et al., 2002; Doran, 2002; Wang and Yang, 2003). The studies in the literature support the idea— when paddy fields receive appropriate farming practices, the result of rice yield potential evaluation that is conducted according to rice’s growth environment (local climate, soil quality, and limiting factors) can serve as a good proxy for local sustainable yield in the foreseeable future. From this argument, this paper suggests the use of the yield potential ranking system that will be described in the following sub-section to estimate the required paddy fields in Taiwan.

provided that the climate pattern is relatively stable and that the soil quality of crop field, once cultivated, is affected mainly by farming practices. According to this argument, the yield potential of a crop, under reasonable and appropriate farming practices, can be estimated through a long-time field study on the relationships between the yield of crop and its established growth environment, that is, local climatic pattern and soil characteristics. A good example for sustaining crop yield through appropriate management practices is the long-term (from 1000 AD to the 1950s) maintained soil fertility and high crop yield in the Tai Lake Region through organic farming practices (Ellis and Wang, 1997). However, this situation has been challenged recently due to soil degradation resulting from modern farming practices (Ellis and Wang, 1997). Management practices can affect crop yield and soil quality, and the application rate of chemical fertilizers is representative of a whole package of practices characterizing a high-yield farming system (Harris, 1996). In terms of supplementing nutrients for crop growth, a positive effect of fertilizers, either chemical or organic, on production could be expected, except when the soil is already damaged or degraded to infertile status. Some studies show that rice production is greater when applying the 100% recommended dose of fertilizers than those with smaller doses, and that the crop production under the application of chemical fertilizers is insignificantly different from those of organic or combined chemical–organic fertilizers (Hegde, 1996; Yadav et al., 2000). The same conclusion was obtained in the comparison of organic and conventional (fossilintensive) production systems (Bulluck et al., 2002). Although the effect of chemical fertilizer on total production has been observed to be positive in recent decades (Harris, 1996; van Vurren and Smith, 2000; Haberl et al., 2001; Tong et al., 2003), the rate of

3.2. Yield-potential ranking system of paddy rice in Taiwan In order to conserve soil fertility and develop suitable farming programs, Taiwan’s government has established a yield potential ranking system of paddy rice according to the results of an 8-year field study, from the second crop of 1977 to the first crop of 1984, with 10,830 sample sites (Chen and Guo, 1991). This yield potential ranking system is shown in Table 1. The system has 10 levels, with each level decreased by 330 kg/ha; this value is 5% of the reference

Table 1 The yield potential ranking system of paddy rice in Taiwan Rank

I

II

III

IV

V

VI

VII

VIII

VX

X

Potential average N6200 6200~5870 5870~5540 5540~5210 5210~4880 4880~4550 4550~4220 4220~3900 3900~3570 b3570 yield Unit—kg/ha. Source: Chen and Guo (1991).

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Table 2 Local climate ranking and its impacts on rice production Climate ranking

Limiting factorsa

Percent decrease in productionb

Rice-yield rankc

1 2 3 4 5 6 7 8

None T1 T2 T 2P 1; E 1; T 3P 1 T 2P 3 E 3; T 1E 2; T 2E 1 E 1P 1; E 2P 1T 1; E 2P 2; E 1P 1W 2; T 1E 2H T 1P 1E 3

0–5 6–10 11–15 16–20 21–25 26–30 31–35 36–40

I II III IV V VI VII VIII

Source: Chen and Guo (1991). a The description of the limiting factors and their individual impacts on rice production are given as the following: Rank of climate factor

1 2 3 b c

Temperature (T )

Precipitation ( P)

Exposure (E)

Range (days)

Production decrease (%)

Range (mm)

Production decrease (%)

Range (%)

Production decrease (%)

46–66 67–85 N85

7.6 12.1 13.4

150–250 250–350 N350

7.1 12.0 16.2

46–50 36–45 30–35

17.6 21.3 26.0

The percentage decrease in production is evaluated against the average yield of the first crop in the Pingtung area. This ranking has not been adjusted according to soil limiting factors.

production, that is, the average yield6 of Pingtung area that was selected as standard-production zone. The criteria for selecting a standard-production zone are (1) its yield has the minimum variance and (2) its average yield is relatively higher among all agricultural areas. The reason for distinguishing adjacent levels by 5% of the reference production is that such an amount of change in paddy rice production attributed exclusively to soil and farming practices is considered very rare in Taiwan, given that the prevalent technology and management of rice cultivation is well developed (Chen and Guo, 1991). The yield potential ranking system of paddy rice was developed primarily on the basis of an agricultural climate ranking system that was established according to the local climate’s impacts on rice production, and was then adjusted through the incorporation of a soil ranking system that was established according to soil quality and hazardous limiting factors (Chen and Guo, 1991). The influence of rice variety and management technology on production was not taken into account, because their improvements have almost reached a climax in Taiwan and a further major advance is not generally

expected in the near future (Chen and Guo, 1991). This yield potential ranking system is characterized by the incorporation of the local climate and rice production, which makes it very practical to farmers. The influences of local climate on reducing the production of rice were evaluated according to temperature, precipitation, exposure, harvest low temperature, and wind (Chen and Guo, 1991), and the results were then arranged into eight levels (see Table 2). The soil quality and limiting factors used in establishing the soil ranking system consist of soil depth; surface soil texture; leaching and root extension; soil drainage and waterlogging; soil fertility, including pH, and cation exchange capacity (CEC)7; salinity; soil nutrients; the damage caused by air, water, and heavy metal pollution; irrigation; environmental hazards and wind; and peculiar characteristics of some types of soil that are beneficial to rice production (Chen and Guo, 1991). The results of soil evaluation were arranged into four levels (see Table 3). The area statistics of paddy fields in Taiwan by location according to this yield potential ranking system are shown in Table 4. In general, the yield of the first crop is higher than that of the second one,

6 Average yield is calculated by excluding the yield resulting from soil with any peculiar character.

7 The nutrients of soil are easier to be absorbed by plants when soil has high value of CEC.

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Table 3 Soil grading standard Soil grade

Description Crop

A B C D

Paddy Paddy Paddy Paddy

rice rice rice rice

Limiting factor or hazards

Soil fertility

The need for improvement

Management

None Medium Severe Significantly severe

High to medium Low Much lower Extremely low

Not necessary Not necessary Require Extremely required

General method Medium care Care Extreme care

Source: Chen and Guo (1991).

Table 4 The statistics of Taiwan’s paddy fields according to its potential yield District

Rank I

II

(A) First crop Taitung Hualian Ilan Taipei Taoyuan Hsinchu Miaoli Taichung 29,395 Nantu 1753 Changhwa 16,922 Yunlin 16,955 Chiayi 11,616 Tainan Kaohsiung 10,391 Pingtung 19,696 Total 107,085 (B) Second crop Taitung Hualian Ilan Taipei Taoyuan Hsinchu Miaoli Taichung Nantu Changhwa Yunlin Chiayi Tainan Kaohsiung Pingtung Total

500

III

IV 2354 14,432 3433

9571 1655 48 1684 361

1199 1045 177 1442 81 14,112

1820 3609 18,748

4155 22,211

40

157

29,520 6281 49,948

500

V

40

85,906

7049 17,677 325 1537 47 202 28 82

VI 329

773 6340 73 328 558 3789 150 746

2588 72 5920 3427

24 182 642 29 159

273 1793 3453 38,393 166 1681

VIII 600 2199 391 285 1030 662 1709

430 13,473

534 47,382

24 6934

952

3787

659 596

2746

755

1216 2884 239 4175 1001 3023 15,071 5678 36,985

10,048 20,156 326 1508 79 1851 858 3923 2030 5745 51,066

5089 40,910 6746 1754 328 99 5415 828 533

1912 1730 15,352 4840 1165 65 24 182 95 863 1346 1777 5552

2553

These figures include the area used for irrigation systems, ridges, houses, and roads. Source: adapted from the appendix in Chen and Guo (1991).

Total area

1603

11,120

Unit: ha 6501 8932 20,743 13,331 45,142 14,217 21,284 41,000 6587 22,346 20,462 12,991 14,557 12,351 33,192 293,636

164 3121

129 7078 7312 3589 5291 956 2606

980

3888

82 485

1878 5234 120

39 721

34

708 47,874

X

VX

329 140 3980 17,206

9581 2509 37 27,897 33,890 31,933 22 22,049 130,471

VII

544 63,501

4332 39,235

525 1255 106

901 116 159 208 289 1580 8424

115

27,076

Unit: ha 10,853 12,525 22,664 17,544 48,621 17,921 24,540 41,153 13,376 57,482 36,328 38,218 44,639 17,412 39,928 443,204

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and the yield of the southern region is higher than that of the north. These areas were measured according to a soil map with a scale of 1: 25,000. The total areas of the first and second crops are 293,636 and 443,229 ha, respectively, which are much larger than those shown in the official statistics of cultivated areas, because they include the land areas occupied by irrigation facilities, ridges, houses, and roads. To obtain the actual area under cultivation, this research refined the figures by deducting a certain percentage of area for each yield potential rank according to official statistics of paddy fields. The results are given in Table 5. The stability of this ranking system was tested by comparing the average yields of the first and second crops in all the agricultural areas for two periods of time: 1977–1980 and 1977–1984 (Chen and Guo, 1991). The results show that, in the case of the first crop, the differences between these two periods range from 1.7% to 3.9%, and in the case of the second crop from 1.5% to 4.23%. The greatest one is smaller than 5%, the selected percentage for level distinction. Thus, the stability of this ranking system seems to be acceptable. In Taiwan the government has advocated organic farming practices since the 1980s. In response to the declining consumption of rice due to a change in diet, the government started an official program to promote rice-field diversion and pause cultivation in 1984. Moreover, to reduce the impact on the local rice market due to the required amount of rice import as regulated by the World Trade Organization (WTO),

Table 5 Yield potential ranking system of paddy fields Rank

Yield potential range (kg/ha)

Area (ha)

I II III IV V VI VII VIII VX Xa

6200 Above 5870–6200 5540–5870 5210–5540 4880–5210 4550–4880 4220–4550 3900–4220 3570–3900 3570 Belowa

99,549 17,385 100,041 165,023 50,143 59,718 102,601 42,720 9278 35,343

a According to 20 years of farming experience in Taiwan, the rice production per hectare equaling to 3600 kg would just about break even; thus, no further levels are distinguished when the yield is lower than 3570 kg.

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the government has launched a program in 1997 to adjust the utilization of paddy and upland fields in order to balance rice demand and supply in the domestic market. Under these programs, the area of cultivated field devoted to green manure crops increased, from 24,666 ha in 1980 to 142,717ha in 2002; during the same period of time, the use of green manure also increased from 278,669 to 2,829,680 metric tons (Council of Agriculture, 2002). This amount of green manure used as fertilizer is much greater than that used in the 1950s when chemical fertilizers were very scarce and expensive in Taiwan. If the negative effects of chemical fertilizers on paddy fields occurred during the time when heavy doses of chemical fertilizers were applied, then we could expect that these programs would be very helpful in mitigating these negative effects. Moreover, the soil quality can be expected to be restored, and even be improved, in the long run. The increase in the use of organic fertilizers and the rotation of pause cultivation further justify the yield potential ranking system of paddy rice as a good proxy for the local sustainable yield of paddy rice in Taiwan in the foreseeable future. In addition, this argument can be supported by additional facts regarding the yield of new rice varieties and the progress of agricultural land consolidation. Taiwan’s recent improvement in rice variety has not significantly changed in terms of production per hectare, compared with that of TaiNong 67(Taiwan Rice Information System (TRIS), 2004),8 on which the adopted yield potential system was established. As for consolidation of agricultural land, which can increase crop yield, it was started in 1958 and accomplished in 1995; in 1984 when the potential yield ranking system was established, about 8 Tai-Nong 67 is the major rice variety in Taiwan in 1980s, and now it still serves as a reference to evaluate new varieties in the official studies on rice improvement. Taking the yield of Tai-Nong 67 as a reference point (index 100), 2-year regional field studies with seven sites showed that the yield index of Tai-Keng 2, 8, 9, and 14 are 104.5, 102.2, 96.9, and 104.0, respectively, for the first crop, and 101.4, 98.4, 94.7, and 99.6, respectively, for the second crop (Taiwan Rice Information System (TRIS), 2004). Among the cases with increased yield, the largest one, 280 kg/ha, corresponding to the first crop of Tai-Ken 2, is still less than 330 kg/ha, the yield difference between adjacent levels of the adopted yield potential ranking system. In recent years, about 80% of the paddy fields in Taiwan were planted with these five rice varieties (Council of Agriculture, 2001b).

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J.-J. Ferng / Ecological Economics 53 (2005) 415–430

Table 6 A hybrid-units transactions table of a hypothetical economy Sector 1 Sector 2 Sector 3

Sector 1

Sector 2

Sector 3

Final demand

Total output

(Units)

Z 11 Z 21 Z 31

Z 12 Z 22 Z 32

Z 13 Z 23 Z 33

Y1 Y2 Y3

X1 X2 X3

(Tons) (Dollars) (Dollars)

80% of agricultural land was already consolidated to form standard farms (Lee, 1996). In addition to serving as proxy for local sustainable yield under appropriate farming practices, the adopted yield potential system can reflect differential productivity, whereas average industrial yield cannot. Choosing a way of resource-area conversion that can reflect differential productivity is important when one conducts dwhat-ifT analysis for estimating the required additional EF in support of the increased consumption of resources. In Section 5, these two kinds of yield will be used in scenario analysis, and the results show that it is important to make selection of yield accord with the purpose of the EF application.

4. Hybrid-units input–output model and embodied resources of manufactured products In the 1970s and 1980s, the estimate of the energy cost, or embodied energy, of sectoral products was a major concern of input–output analysis (Bullard and Herendeen, 1975; Herendeen, 1978; Miller and Blair, 1985). In general, economic sectors face differential energy prices. Thus, estimating the embodied energy of sectoral products by directly pre-multiplying a standardized Leontief inverse matrix with a vector of energy input coefficients could generate significant bias (Miller and Blair, 1985). A hybrid-units modeling method was suggested by Bullard and Herendeen (1975) to solve this problem, whereby a monetaryunit input–output table is modified by recording the row vector(s) of concerned sectoral products, which are used as intermediate inputs and delivered to final demand, by physical units. For example, in the calculation of embodied energy, heat units instead of monetary units are used to measure the row vectors of coal and petroleum in an input–output table. One may refer to Miller and Blair (1985: 200–235) for a detailed discussion on the advantages of hybrid-units input–output analysis, compared with monetary

input–output analysis, in estimating the embodied energy of sectoral products. A physical input–output analysis can indeed provide a more accurate physical relationship between input and product. However, only a few European countries have regularly published biophysical input– output tables since the 1990s; moreover, no uniform format of data recording has been established (Hubacek and Giljum, 2003). With the increasing interests in estimating the total required resources for supporting a particular consumption pattern and/or production activity, a hybrid-units input–output model could be a good proxy tool when the amount of concerned resources in physical terms can be obtained directly from official statistics or indirectly estimated by using the sectoral differential prices. The following briefly describes the use of hybridunits input–output analysis to estimate the total consumption of concerned resources in mathematical terms. Table 6 gives the hybrid-units transaction table of a hypothetical three-sector economy, in which Sector 1’s product is the raw material of concern and it is measured by tons, while the products of Sectors 2 and 3 are manufactured goods and measured by dollars. The matrix of input coefficients, denoted by A, the Leontief inverse matrix, (I
A)
1, and the vector of total production, X, can be obtained by Eqs. (1)–(3), respectively.9 A ¼ Z ðdiagonal X Þ
1 P P P

½ ½

9

½ 

Z11 ¼ Z21 Z31

Z12 Z22 Z32

Z13 Z23 Z33

a11 ¼ a21 a31

a12 a22 a32

a13 a23 : a33

X1 0 0

0 X2 0

0 0 X3




1

ð1Þ

The notations of the matrices used in the hybrid-units input– output model are underlined to distinguish themselves from those in the standardized one.

J.-J. Ferng / Ecological Economics 53 (2005) 415–430

ðI
P A Þ
1 ¼

f½

1 0 0

2

a11 ¼ 4 a21 a31

0 1 0

½

0 a11 0
a21 1 a31

a12 a22 a32

a13 a23 a33

g


1

3 a13 a23 5: a33

a12 a22 a32

½

a11
1 ¼ ðI
A Þ Y ¼ a21 X P P P a31

a12 a22 a32

ð2Þ

½ 

a13 a23 a33

Y1 Y2 Y3

2

3 a11 Y1 þ a12 Y2 þ a13 Y3 ¼ 4 a21 Y1 þ a22 Y2 þ a23 Y3 5: a31 Y1 þ a32 Y2 þ a33 Y3

½

tons $ $



tons $ ; $

5. Case study: the required area of paddy fields in Taiwan

½ 

tons $ Y ¼ ; P $

2

3 tons X ¼ 4 $ 5: P $ The units of input-coefficients matrix A are thus Z ðdiagonalX Þ
1 A ¼P P

P

½

tons=tons ¼ $=tons $=tons

by tons) required in the production of one unit of Sector j’s product (tons for Sector 1 and dollars for Sectors 2–3). The elements of (I
A)
1, a ij , represent the amount of Sector i’s product (tons) needed to deliver one unit of Sector j’s product (tons for Sector 1 and dollars for Sectors 2–3) to final demand. In this hypothetical three-sector economy, Sector 1’s product represents the raw material of concern and then the amounts of this resource required for delivering one dollar-value of the products of Sectors 2 and 3 to final demand are depicted by a 12 and a 13, respectively. The total amount of the concerned raw material required for supporting the final demand of the hypothetical economy, Y, is the first element of vector X; that is, a 11Y 1+a 12Y 2+a 13Y 3, as shown in Eq. (3).

ð3Þ

The hybrid-units input–output model distinguishes itself from the monetary-unit counterpart through the use of physical units in measuring certain rows of the data in a transactions table. The units of the matrices used in formulating a hybrid-units input–output model can be depicted by tons Z $ ¼ P $

425



tons=$ tons=$ $=$ $=$ : $=$ $=$

The units of the Leontief inverse matrix, (I
A)
1, are the same as those in matrix A. The input coefficients a ij , as shown in Eq. (1), represent the amount of Sector i’s product (measured

This paper explores the required area of paddy fields in Taiwan according to two scenarios. Scenario (I) concerns the required area for supporting direct and indirect consumption of paddy rice by Taiwan’s residents in 1996. Scenario (II) explores the additional area of paddy fields that would be needed when the food energy from cereals is provided exclusively by rice and the import of rice is not possible. A hybrid-units input–output table was constructed by measuring the amount of rice used as intermediate input and final demand in metric tons instead of NT dollars. Some of the required data, such as the amount of imported and exported rice in physical terms, can be obtained directly from official statistics. Others can only be indirectly estimated through average prices; wholesale price was used in the case of inter-industrial transactions and retail price for household consumption. The price of paddy rice that was used as feed was determined according to government regulation. According to this established hybrid-units input– output table, the direct and indirect rice consumption of households and the government under Scenario (I) was estimated, following the procedure described in Section 4. The result shows that the total demand of paddy rice by households and the government in 1996 was 2026.75 thousand metric tons. The required amount of paddy rice under Scenario (II) was obtained

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J.-J. Ferng / Ecological Economics 53 (2005) 415–430

directly by dividing the total demand of paddy rice under Scenario (I) by 0.6238,10 which is the ratio of food energy from rice to that from total cereals. The result was 3249.29 thousand metric tons of paddy rice. The areas of paddy fields required to support the total demand of paddy rice under Scenarios (I) and (II) were estimated according to the yield potential ranking system of paddy rice as shown in Table 5.11 The area available for each yield potential rank was seen as the upper limit of field availability in the calculation. The calculation of required paddy fields followed a btop–downQ sequence; that is, the area of higher ranked paddy fields was assigned prior to that of lower ranked ones. In theory, this calculation procedure is in accordance with the cultivation of marginal land. In practice, this method is consistent with Taiwan’s policy on rice-field diversion and pause cultivation, in which priority is given to the lower yield fields and to the second-crop fields that usually have a higher growth cost than that of first-crop fields. Moreover, this calculation sequence also agrees with Taiwan’s policy on the release of agricultural land to other types of land use, such as housing, in which only low-productivity land is released. Table 7 presents the calculation of the required area of paddy fields under Scenarios (I) and (II). Except for ranks I and X, the middle value for each range of yield potential was used to calculate the total rice production provided by the paddy field of that rank. As mentioned before, a higher yield field is generally devoted to cultivation prior to a lower one; thus, corresponding to each rank the cumulated production, which includes the production of the preceding higher ranked fields, was calculated. The last two columns of Table 7 show the areas, by rank and in total, required to grow paddy rice under Scenarios (I) and (II). Recall that the total demand of paddy rice under Scenarios (I) and (II) are 2026.74 and 3249.29

Table 7 Required paddy fields under Scenarios (I) and (II) Rank

I II III IV V VI VII VIII VX X

Yielda (kg/ha)

Cumulated productionb (1000 metric tons)

Required paddy field (ha) Scenario (I)

Scenario (II)

6200 6035 5705 5375 5045 4715 4385 4060 3735 3570

617.20 722.12 1292.85 2179.85 2432.83 2714.40 3164.30 3337.75 3372.40 3498.57 Total

99,549 17,385 100,041 136,538

99,549 17,385 100,041 165,023 50,143 59,718 102,601 20,933

353,513

615,392

a

In the case of Ranks II to VX, the yield was chosen according to the middle value of the corresponding range; while the maximum and minimum value was chosen for ranks I and X, respectively. b Except for Rank I, the cumulated production for each rank includes the production from the upper levels.

thousand metric tons, respectively, the ratio of the latter to the former is 1.60. However, the ratio of the required area of paddy field under Scenario (II) to that under Scenario (I) is 1.74. The difference between these two ratios reveals that the additional requirement of paddy fields under Scenario (II) cannot be extrapolated directly according to the relationship between the demand of paddy rice and the resulting required area of paddy fields shown in Scenario (I). Moreover, the difference in such ratios is expected to increase when more additional rice consumption is needed, because higher yield fields are usually already devoted to cultivation. From the viewpoint of food supply security, the results of these two scenarios shed light on the importance of the long-term preservation of paddy fields in Taiwan. If one estimates the required paddy fields according to the average industrial yield obtained by dividing the total production by the total planted area, that is, 5818 kg/ha in 199612, the estimated required

10

According to Taiwan’s Food Supply and Utilization Yearbook (Council of Agriculture, 2001a), the average amount of calories from rice and its products for Taiwan residents in 1996 is 562 Kcal/ day per capita, which is 62.38% of the total calories from cereals. 11 The rice yields of imported countries were treated equal to that of Taiwan because this study examined the amount of domestic paddy fields needed for meeting the total demand of rice.

12

According to government statistics, the productivity of brown rice in 1996 is 4,536 kg/ha. This value was converted into that of paddy by dividing it with 0.78, the percentage of brown rice used by the government, so as to accord with the unit used in the adopted yield potential ranking system.

J.-J. Ferng / Ecological Economics 53 (2005) 415–430

areas can be different. As shown in Table 8, the required areas are 348,515 and 558,740 ha for Scenarios (I) and (II), respectively, rather than 353,513 and 615,392 ha (Table 7). The estimated areas according to the adopted yield potential ranking system are larger than those obtained according to the average industrial yield in 1996; the difference is 4998 ha for Scenario (I) and 56,653 ha for Scenario (II). The ratios of the area estimated from the yield potential ranking system to that from average industrial yield are 1.01 and 1.10 for Scenarios (I) and (II), respectively. Obviously, the difference between the results obtained from the use of yield potential ranking system and average industrial yield under Scenario (I) is much smaller than that under Scenario (II). This small difference can be attributed to the fact that the government has promoted rice-field diversion, pause cultivation, and organic farming practices since the 1980s such that currently planted areas would correspond well to those fields with higher yield potential, and that current industrial yield is not obtained mainly through over-exploitation practices. Thus, the small difference under Scenario (I) cannot be seen as an excuse for neglecting the distinction between sustainable yield and industrial yield in the EF application where resource over-exploitation is of concern. When comparing the estimates under Scenarios (I) and (II), the results clearly show that the required paddy fields could be well underestimated if one does not take into account the differential yields among paddy fields. This suggests that one needs to pay more attention to the values of resource-area conversion factor adopted in the EF analysis when he conducts a dwhat-ifT analysis, in which scenario is concerned with additional consumption of resources. This suggestion also applies to the studies concerning inter-generational equity, in which the adoption of sustainable yield and considTable 8 Comparison between yield potential system and industrial yield

Scenario (I) Scenario (II) Unit—ha.

Yield potential system (A)

Industrial yield (B)

(A)–(B)

(A)/(B)

353,513 615,392

348,515 558,740

4998 56,653

1.01 1.10

427

eration of differential productivity can significantly affect the estimates of EF.

6. Conclusion The EF analysis has been used to address two important dimensions of sustainable development: intra- and inter-generational equity on the appropriation of ecosystem services. In terms of intra-generational equity, previous EF applications have successfully revealed the unequal share of global ecosystem services by comparing per capita consumption–EF among countries. With regard to the issues on inter-generational equity, the standardized EF calculation method may fail in this regard, because industrial yield is assumed to be sustainable such that no attention paid to the farming practices that over-taxes soil, and industrial yield is calculated in terms of average value from which differential productivity cannot be revealed. In estimating the required paddy fields for supporting the total rice consumption of Taiwan residents in 1996, this paper approximates the local sustainable yield according to a yield potential ranking system of paddy rice, which was developed by the government from the relationships between the local climate, soil property, and rice production obtained through an 8-year field study. According to the reviewed literature on the relationships between yield, farming practices, and soil quality, the progress of consolidation of agricultural land in Taiwan, and the yields of her new rice varieties, one may reasonably expect that the production potential revealed by this ranking system, under appropriate farming practices, can be maintained in the foreseeable future. The total demand for paddy rice was estimated using a hybrid-units input–output method, in which physical units, instead of monetary units, measure the consumption of paddy rice in an input–output table. Moreover, two scenarios of rice consumption were proposed. Scenario (I) estimated the amount of rice consumed directly, and indirectly through rice products, by Taiwan residents according to her input–output table of 1996. Scenario (II) concerned the required total rice consumption by Taiwan residents in 1996 if the food energy from cereals

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J.-J. Ferng / Ecological Economics 53 (2005) 415–430

is supported exclusively by rice and the import of rice is not possible. In the scenario analysis, both the yield potential ranking system and average industrial yield of 1996 were used to estimate the required paddy fields. In both scenarios, the estimated areas were larger when using the yield potential ranking system; and their difference in Scenario (II) was larger than that in Scenario (I). The results suggest that more attention needs to be directed to the selection of yield in the EF application in which over-tax of soil is of concern and differential productivity is significant. This suggestion also applies to the EF application when the current industrial yield of study area is realized primarily through over-exploitation practices. In practice, the result of Scenario (I) can be used in the program for allocating paddy fields into pause cultivation so that the cultivated paddy fields can support the domestic demand of rice consumption in the context of the changing diet of Taiwan residents and the rice import regulation of WTO. The result of Scenario (II) shows a possible reliance on paddy fields when the stability of food supply is of concern, thus supporting the long-term preservation of existing paddy fields in Taiwan. The paddy fields in Taiwan face growing pressures from land use conversion. The pressures come from the heavy demand for low-price, built-up land and the import of low-price crops regulated by WTO. In addition, the average consumption of rice per capita has decreased due to a change in diet. These pressures pushed the government to formulate a program to release agricultural land into other types of land use in 1995. The difference in the required paddy fields between the two scenarios may shed light on the importance in preserving paddy fields so that they can be put into cultivation immediately when required. Moreover, the ecosystem services supported by agricultural land such as open space and water conservation are thus maintained at the same time.

Acknowledgements Financial support for this research was provided by the National Science Council, Executive Yuan of the

Republic of China (Grant No: NSC 90-2415-H-305006). The author gratefully acknowledges the two anonymous referees, whose constructive comments greatly improve the manuscript.

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