SCOPE 29 - The Greenhouse Effect, Climatic Change, and Ecosystem

	9.1 INTRODUCTION
	9.2 THE DIRECT EFFECTS OF INCREASED CO2
		9.2.1 Biochemistry and Physiology of CO2 Responses
		9.2.2 Biological Feedbacks
		9.2.3 Growth and Yield Under Good Environmental Conditions
		9.2.4 Interaction with Growth-limiting Environmental Factors
		9.2.5 Summary
	9.3 PERSPECTIVES ON CLIMATE IMPACTS
		9.3.1 The Slow Change View
		9.3.2 The Shift-in-risk View
		9.3.3 Blending the Views: Adaptation and Adjustment
	9.4 THE IMPACTS OF CLIMATIC CHANGE
		9.4.1 Crop Impact Analysis
		9.4.2 MarginalSpatial Analysis
		9.4.3 Agricultural Sector Analysis
		9.4.4 Historical Case Studies
	9.5 SUMMARY AND CONCLUSIONS
		9.5.1 The Possible Impacts of Increased CO2 and Climatic Change
		9.5.2 Further Considerations
		9.5.3 Some Next Steps
	NOTE ON AUTHORSHIP AND ACKNOWLEDGEMENTS
	9.6 REFERENCES

9.1 INTRODUCTION

As discussed in the previous chapter, climate plays a major role in determining the yield levels, the year-to-year variability and the spatial patterns of global agriculture. Increases in atmospheric CO₂ concentrations and changes in climate could thus have far-reaching implications for international food production and security. From a global perspective the issues vary from the temperate zones to the tropics, from the core crop regions to the margins of production, and from the developed to the developing countries. The purpose of the present chapter is to gather together what we know (and point out what we do not know) about the effects of CO₂ and climatic change, and to elucidate the broad approaches for addressing these issues.

This review is selective in its treatment while attempting to maintain a global perspective. The emphasis is placed on food crops, particularly grains, to the neglect of livestock, grasslands and fibre crops. This is not only because grains account for the vast bulk of global food production and serve to link world regions through international trade, but also because existing models and studies of the agricultural impacts of CO₂ and, especially, climatic change have focused largely on these crops. As a whole these studies jump scales to address relevant research questions at different levels of organization ranging from the plant to global food trade; as a consequence, so does this chapter.

The chapter begins by reviewing the evidence for the direct effects of higher CO₂ concentrations on individual plant processes, growth and yield. In Section 9.3 we turn to the subject of the agricultural impacts of long-term climatic change. In this section we consider alternative ways of formulating the problem in the light of the complexities created by short-term climatic variability and the response capabilities of agriculture itself. This is followed in Section 9.4 by a discussion of the major approaches to impact assessment and the results of specific analyses. In the concluding section we summarize the main findings and suggest some general directions for future research.

9.2 THE DIRECT EFFECTS OF INCREASED CO₂

In contrast to climatic changes, the rise in atmospheric CO₂ itself will be comparatively smooth and continuous with little year-to-year variability. How will higher CO₂ concentrations affect crop yields in the future in the absence of consideration of climatic change?

Much of the discussion of direct biological effects focuses on the impact of a CO₂ doubling. Unfortunately, there has been no consistency as to the control or base concentration used for purposes of comparison. Although the global average is now 343 ppmv (in 1984), the exact concentration varies with season and latitude (Figure 9.1a), with height above ground and with time of day (Figure 9.1b). Base CO₂ levels used in experiments have varied from 270 to 340 ppmv, and since the response of plant photosynthesis to CO₂ concentration is a saturating one (Figure 9.1c), the reader is cautioned that the quantitative effect of a CO₂ doubling can vary for that reason alone.

The reduction of CO₂ to carbohydrates by photosynthetic carbon fixation accounts for about 90% of the accumulation of plant dry matter. That CO₂ concentration is a limiting factor is shown by the numerous experiments in which higher CO₂ enhances photosynthesis and crop growth. The response has been found to hold even for plants grown under a variety of stressful conditions, despite a frequently repeated generalization that relates to the so-called `law of limiting factors'. This idea is that when other environmental factors such as water shortage, low light, mineral shortage or excess, and non-optimal temperature limit yield, then higher CO₂ concentration will have little or no effect. Although the generality of that concept has been challenged (Gifford, 1977, 1979a, 1979b, 1980a; Pearcy and Bjorkman et al., 1983) the idea persists (e.g. Kramer, 1981; Liss and Crane, 1983; Tolbert et al., 1983). Indeed, in certain stressful environments the relative photosynthetic response of plants to CO₂ enrichment is actually increased (as is noted below).

Predictions of crop growth and yield are incomplete if based solely on the photosynthetic response at the level of the primary CO₂ fixation mechanism. Other primary and secondary responses (like stomatal conductance and morphological development) and feedbacks interpose between photosynthetic metabolism and crop yield and must be taken into consideration in assessing the effects of higher atmospheric CO₂. Our current understanding of these processes and their effects is reviewed below, building from the underlying biochemical and physiological responses through to field crop yield, with and without limitations imposed by other environmental restraints.

Figure 9.1 Some examples of variations in CO₂ concentrations (a) seasonally, (b) diurnally in an alfalfa crop at Mead, Nebraska, U.S.A., and of (c) net photosynthetic response curves to CO₂ concentration of a C₃ species (wheat) and a C₄ species (maize)

9.2.1 Biochemistry and Physiology of CO₂ Responses

Plants are grouped photosynthetically into three groups-'C₃', 'C₄' and 'CAM'-according to biochemical distinctions in the mechanism of primary CO₂ fixation. Pineapple is the only representative among commercial crops of CAM plants and would not be expected to respond appreciably to higher CO₂, so it will not be discussed further. At least 95% of the world's biomass is of the C₃ category as are most crop species (Figure 9.2).

The distinctions between the groupings derive largely from the enzymes involved in photosynthetic fixation. Only two, perhaps three, enzymes are known to be of significance in the response of plants to CO₂ enrichment: 'rubisco',¹ 'PEP carboxylase'² and perhaps carbonic anhydrase. Rubisco is the primary enzyme for photosynthetic fixation in the C₃ plants and the CO₂ fixation rate per unit leaf area is typically related positively to the amount of this enzyme per unit leaf area. Carbon dioxide itself (together with Mg⁺⁺) activates rubisco by binding at a non-catalytic site on the enzyme protein (Jensen and Bahr, 1977). Rubisco may not always be fully activated in vivo for leaves in normal air (Perchorowicz et al., 1982; Vu et al., 1983), but it is not known whether higher CO₂ concentrations increase the level of activation in the long term (i.e. over the seasonal growth cycle).

Rubisco is not only responsible for catalysing the reduction of the CO₂ to carbohydrates, but, in the presence of light, catalyses a reaction with oxygen. The metabolite produced by the oxygenation releases recently fixed CO₂ during its metabolismthe process of photorespiration. The same catalytic site on rubisco binds both O₂ and CO₂. In C₃ plants the photorespiration rate is high and is determined in part by the relative proportions of CO₂ and O₂ in the leaf. Some of the response of photosynthesis to higher CO₂ concentration is believed to derive directly from the improved competitive advantage of CO₂ molecules over O₂ molecules for the active sites on rubisco. The reduced carbon flow through the photo respiratory cycle leads to less photorespiratory CO₂ loss as well. Even in the absence of competing O₂, however, the fully activated enzyme in leaves at 340 ppmv CO₂ is probably operating only at about half to three-quarters of its substrate-saturated capacity (Edwards and Walker, 1983).

In contrast, the primary carboxylase in C₄ plants is PEP carboxylase which is not competitively inhibited by O₂. Photorespiration is therefore negligible. PEP carboxylase has a higher effective affinity for CO₂ than does rubisco in the absence of O₂, so the enzyme is close to CO₂-saturation at the present atmospheric CO₂ concentration. Therefore one would not expect a significant enhancement of C₄ crop growth from increased CO₂ in so far as the primary carboxylase properties are concerned.

¹ Ribulose 1,5-bisphosphate carboxylase-oxygenase (EC4.1.1.39 or 'rubisco').

²Phosphoenolpyruvate carboxylase (EC 4.1.1.31 or 'PEP carboxylase').

Figure 9.2 Examples of C₃ and C₄ crops and their global annual production (fresh weight) as reported by FAO Production Yearbook, 1980 (adapted from Swaminathan, 1984)

Leaf surfaces are covered with microscopic pores, or stomata, through which gaseous exchange occurs. The aperture of the pores varies, striking a balance between inwards diffusion of CO₂ and outward diffusion of water vapour (transpiration). Higher atmospheric CO₂ concentration reduces stomatal aperture, thereby reducing transpiration. Hence the efficiency of water use in photosynthetic carbon fixation (`water use efficiency') is increased. The biochemical mechanism of stomatal response to CO₂ is unknown.

There is no difference between C₃ and C₄ plants with respect to the sensitivity of stomatal conductance to change in CO₂ concentration (Morison and Gifford, 1983). This view contradicts a common assertion that C₄ species display greater sensitivity. The assertion derives from leaf chamber studies from which are observed considerable variations among species in the absolute value of stomatal conductance in 'normal' CO₂ for particular combinations of genotype, development stage and environmental conditions. If, however, one focuses on the relative sensitivity of stomatal conductance in control- vs increased-CO₂ experiments, there appears to be little difference between the plant groups (Morison 1985). A reasonable approximation is that for most species and environmental conditions, a CO₂ doubling will cause about a 40% decrease in stomatal conductance, at least in the short term.³

Experiments on the effects of high CO₂ concentrations on dark respiration show mixed results. It has been proposed that mitochondrial respiration may increase in plants under high CO₂ in response to sucrose accumulation in leaves (e.g. Tolbert et al., 1983). A mechanism for this is thought to act via the 'alternative pathway of respiration', a normal mechanism that may function to dissipate excess photosynthesized energy (Lambers, 1982). This proposal is consistent with the findings of Hrubec et al. (1984), for example, who reported increased respiration rates of soybean leaves grown in high CO₂. However, the converse result was found for wheat (Gifford et al., 1985); plants grown continuously in 590 ppmv CO₂ experienced half as much whole-plant respiration by night (per unit net carbon fixed by day) as did plants grown in normal air. Whether a primary or secondary response, any inhibition of respiration will contribute to the stimulating effect of high CO₂ on net carbon gain while increased respiration will detract from it.

With respect to morphology and development, some species grown in high CO₂ experience greater leaf area expansion and advanced time of flowering (e.g. Hand and Postlethwaite, 1971; Goudriaan and de Ruiter 1983). Although such effects are presumably often a response to improved photosynthate supply, there are also indications of a less direct CO₂ effect. In C₄ species that do not respond photosynthetically to high CO₂, leaf area has been observed to increase. For example, growth analysis of both maize (Imai and Murata, 1978) and itchgrass (Patterson and Flint, 1980) showed that leaf area increased while net dry weight (DW) gain per unit leaf area (`net assimilation rate') was unaffected by CO₂ enrichment to above 600 ppmv. Similarly, with a doubling of normal CO₂, Morison and Gifford (1984b) observed increases in leaf area of the C₄ species Amaranthus edulis (15%), Sorghum bicolor (29%) and Zea mays (40%) grown on declining soil water content. At the same time, the efficiency of conversion of intercepted radiation into dry matter was unchanged by the high CO₂. Thus the increase in growth caused by higher CO₂ in these C₄ species was attributable to greater interception of light because of bigger leaf area, not to increased photosynthesis per unit leaf area. This implies that CO₂ was acting on leaf area development in some way other than via CO₂ effects on photosynthesis rate.

³ Morison (1985) plotted conductance at 660 ppmv CO₂ against conductance at 330 ppmv for 80 observations from the literature covering a wide range of species (C₃ and C₄), conditions and methodologies. There was linear correlation through the origin over a 20-fold conductance range, with conductance at 660 ppmv being 0.59 ± 0.04 (99% confidence limit) of conductance at 330 ppmv.

Effects of CO₂ on flowering time are usually minor but not necessarily solely due to change in photosynthate supply. For example, a slowing in the rate of flower development in sorghum without any change in dry weight growth (Hesketh and Hellmers, 1973; Marc and Gifford, 1983), seems indicative of some more direct influence of CO₂ on flowering.

9.2.2 Biological Feedbacks

Although leaves of C₃ species photosynthesize faster when transferred to an atmosphere containing higher CO₂ concentration, this initial response may not necessarily persist. Over the growing cycle of the plant, biological feedbacks can come into play acclimating enzymatic activities and leaf photosynthetic rates to the CO₂-enriched environment. But reports on the subject of photosynthetic acclimation offer no consistency as to the direction of change. For example, leaf photosynthetic capacity in high CO₂ concentration has been shown to be higher than (e.g. Bishop and Whittingham, 1968), the same as (e.g. Gifford, 1977), and lower than (e.g. von Caemmerer and Farquhar, 1984) the capacity of plants grown in normal air.

These differences may arise from the interaction of multiple feedback mechanisms. Understanding of photosynthetic acclimation to high CO₂ is hindered by the fact that most reports do not permit separation of effects operating just at the enzymatic and leaf levels, from the longer-term effects emanating from changes in the 'source:sink balance' in the whole plant. Maintenance of enhanced photosynthesis rates and, eventually, yield depend on an adequate sink (or storage organ, like the grain) for the photosynthates. If the growth of sinks does not respond to higher photosynthate supply, then photosynthesis can be depressed a negative feedback. This occurs, perhaps, by build-up of photosynthetic products in the leaf (Madsen, 1968; Herold, 1980), although the exact mechanism behind this feedback is still unknown (Gifford and Evans, 1981).⁴

Despite limited information, such results suggest that several feedback mechanisms can develop within the CO₂-enriched plant, and that the balance between them varies during plant development and determines the photosynthetic acclimation to higher CO₂. A clearer understanding of this process will depend in part on the ability of further research to separate the influences of these feedback mechanisms on photosynthetic response over time.

An increased rate of senescence (aging) is another possible feedback effect of CO₂ enrichment. Accelerated senescence has been observed in two winter annual species (St. Omer and Horvath, 1983) and in cotton (Chang, 1975), the latter exhibiting concurrent decline in carbonic anhydrase activity in the leaves. Although the observed senescence effect is minor, and is not always detected (e.g. no effect in wheat (Krenzer and Moss, 1975; Gifford, 1977)), it could possibly be pervasive due to increase in ethylene, a natural growth regulator in plants which accelerates senescence. High CO₂ concentrations caused sunflower plants to produce more ethylene, for instance (Dhawan et al. 1981). In addition, the CO₂ source for enriching the air might also contain unsuspected traces of ethylene which could promote early senescence. Some Australian Sources of CO₂, for example, contained traces of ethylene which were sufficient to hasten senescence of some species (e.g. tomato) but not others (e.g. maize) (Morison and Gifford, 1984a).

If some CO₂-stimulated DW growth were invested in leaf area expansion, a positive feedback effect could be established. Expanded leaf area would allow greater light interception which would promote further DW growth, additional leaf area expansion, and so on, until the leaf canopy becomes dense enough for full interception of incident radiation. Experimental results vary. Soybean and sunflower grown at twice normal CO₂ did not develop more leaf area in some experiments (Carlson and Bazzaz, 1980; Marc and Gifford, 1984), but did in others (Rogers et al., 1984; Morison and Gifford, 1984b). Rice frequently does not increase leaf area appreciably under CO₂ enrichment even though DW growth responds (Yoshida, 1972; Imai and Murata, 1978; Morison and Gifford, 1984b). Conversely, several C₄ species that did not show a response of net CO₂ fixation per unit leaf area or per unit of intercepted radiation, nevertheless responded with an increase in leaf area (as noted above).

The mechanisms involved in CO₂-stimulated leaf area expansion have not been widely investigated. They could be expected to vary, however, since it is known that, depending on the species, the component of leaf area increase under CO₂ enrichment varies between axillary growth (branching; Johnston, 1935), faster rate of leaf emergence (Hofstra and Hesketh, 1975) and development of larger leaves (Goudriaan and de Ruiter, 1983).

⁴ It is interesting to note, however, that in one experiment in which the sink:source rate in soybean was lowered surgically, enhanced leaf photosynthesis persisted for many weeks at high CO₂ levels (as compared to control-CO₂), despite the inability of the remaining sinks to accept more photosynthetic assimilate (Peet, 1984).

9.2.3 Growth and Yield Under Good Environmental Conditions

Under favourable growing conditions, what can we say about the net effect of all the aforementioned responses and feedbacks on plant growth and yield? One attempt to summarize CO₂ enrichment experiments showed mostly positive effects (and a few negative effects) across all groups of C₃ species. Kimball (1983) interpolated 134 observations from the CO₂ enrichment literature published over 64 years to ascertain the average increase of DW growth and yield of a variety of species in response to double `normal' (330 ppmv) atmospheric CO₂ (Table 9.1). Most of the experiments were conducted under 'good' conditions of nutrient and water supply. For the average of all C₃ species investigated, economic yield increased 26% and immature shoot dry weight increased 40%.

One particularly interesting finding concerns the growth response of small grains. Immature DW (biomass) generally exhibits greater response to high CO₂ than the final economic yields, but this is not so for small grain cereals like wheat. As shown in Table 9.1, the high (36%) increase in grain yields with a CO₂ doubling is nearly twice the increase in biomass of immature crops (20%), a finding that is also supported by work of Goudriaan and de Ruiter (1983). The effects of high CO₂ on wheat seedlings is small (Neales and Nicholls, 1978) compared to the effects once tillering and grain formation occur (Gifford, 1977; Sionit et al., 1981a). This might be a reflection of the powerful influence of CO₂ enrichment before ear emergence on tillering and sink size (ear number) in small-grain cereals (Gifford et al. 1972; Cock and Yoshida, 1973). This result with cereals belies the tentative generalization (Kramer, 1981) that determinate species (i.e. those for which leaf development ceases after flowering, as in cereals) respond less to CO₂ enrichment than do indeterminate species. Given the central role that small grains play in world food production and trade (see Chapter 8), this finding could prove to be of special importance in a CO₂-enriched future.

For C₄ species, the results are mixed. Some growth experiments (Marc and Gifford, 1984; Gifford and Morison, 1985) confirm the biochemically derived expectation of no appreciable growth response of well-watered plants to high CO₂. However, other examples (cited by Kimball, 1983; Morison and Gifford, 1984c) show substantial CO₂ effects on growth and yield. There are two routes whereby this could occur: by some unknown non-photosynthetic effect of high CO₂ on leaf area expansion and hence on light interception (see Section 9.2.1), or via an interaction with water stress, as discussed below. 

Table 9.1   Mean predicted growth and yield increases for various groupings of C₃ species for a doubling of atmospheric CO₂ concentration from 330 ppmv to 660 ppmv (adapted from Kimball, 1983). the errors indicated are 95% confidence limits

	Footnote	Immature crops		Mature crops
		No. of  records	% increase of biomass	No. of  records	% increase of marketable yield
Fibre crops	1	5	124	2	104
Fruit crops	2	15	40	12	21
Grain crops	3	6	20	15	36
Leaf crops	4	5	37	9	19
Pulses	5	18	43	13	17
Root crops	6	10	49
C3 weeds	7	10	34
Trees	8	14	26
Av. of all C3		(83)	40 ± 7	(51)	26 ± 9
Footnotes: The species represented are:
1. cotton (Gossypium hirsutum);
2. cucumber (Cucumis sativus), eggplant (Solanum melongena), okra (Abelmoschus
esculentus), pepper (Capsicum annuum), tomato (Lycopersicum esculentum);
3. barley (Hordeum vulgare), rice (Oryza sativa), sunflower (Helianthus annuus), wheat
(Triticum aestivum);
4. cabbage (Brassica oleracea), white clover (Trifolium repens), fescue (Festuca elatior),
lettuce (Lactuca sativa), Swiss chard (Beta vulgaris);
5. bean (Phaseolus vulgaris), pea (Pisum sativum), soybean (Glycine max);
6. sugar beet (Beta vulgaris), radish (Raphanus lativus);
7. Crotalaria spectabilis, Desmodium paniculatum, jimson weed (Datura stramonium),
pigweed (Amaranthus retroflexus), ragweed (Ambrosia artemisiifolia), sicklepod (Cassia
obtusifolia), velvet leaf (Abutilon theophasti);
8. cotton (Gossypium deltoides).

9.2.4 Interaction with Growth-limiting Environmental Factors

Under controlled-environment conditions, the percent enhancement of growth owing to high CO₂ concentration has been found to be greater with restricted water supply than with unlimited watering. Since higher CO₂ reduces stomatal conductance (by about 40% for a CO₂ doubling), water use efficiency in the production of dry matter (WUE) increases with CO₂ concentration, even for C₄ species. However, the relative reduction of transpiration rate per unit leaf area is not as great as that for stomatal conductance because, with reduced evaporative cooling, leaf temperature increases, thereby increasing the driving force behind transpiration (viz. the leaf-to-air vapour pressure difference) (Morison and Gifford, 1984c). Thus doubling the CO₂ concentration reduced stomatal conductance of sorghum by 40%, but transpiration rate by only 15% (van Bavel, 1974).

Under growth-limiting water supply, growth of C₃ crops responds to higher CO₂ because of both photosynthetic and stomatal effects (Gifford, 1979a; Morison and Gifford, 1984c), while growth of C₄ species responds because of stomatal effects alone. Thus for both C₃ and C₄ species, the less the availability of water, the greater the percent increase (`relative enhancement') of growth by high CO₂ concentrations.

However, in scaling up from controlled environment to a wide expanse of vegetation in the field, other attenuating phenomena come into play to determine the rate of transpiration. In circumstances where boundary layer conductance is low relative to stomatal conductance (i.e. non-windy conditions), not only is the role of stomata] conductance in controlling transpiration attenuated, but also leaf temperature is higher than under windy conditions and atmospheric humidity close to the crop surface can increase. All these aspects would reduce the impact of CO₂-induced stomatal closure on transpiration. Furthermore, and perhaps even more powerfully, soil water content may be a more important determinant of rate of transpiration on a time scale of days to weeks than is stomatal response to attributes of the aerial environment. For example, for 16 species, the time-course of depletion to exhaustion of stored soil water by individual plants was little affected by twice normal CO₂ because the high CO₂-induced leaf area increases compensated for the reduction in transpiration rate per unit leaf area (Morison and Gifford, 1984b).

Simulation of the CO₂ effect under optimal supply of water also indicated a compensation of decreased leaf transpiration by increased leaf area, resulting in a practically constant transpiration rate per ground area. In other words, the increase in overall water use efficiency approximately equalled the increased growth rate (Goudriaan et al., 1984). In this sense, higher atmospheric CO₂ concentrations may not reduce the frequency of agricultural droughts, as some have claimed. Rather, droughts will still occur but at a higher level of biomass.

Carbon dioxide enrichment increases crop growth and yield at low light intensity which is itself severely growth limiting. The relative enhancement of growth can even be greater than at high light level, as has been found for wheat (MacDowell, 1972; Gifford, 1979a). There are at least two aspects to the mechanism of growth response to CO₂ under photosynthetically limiting light intensities. One is that the quantum yield of leaf photosynthesis close to the light compensation point (i.e. the light intensity at which CO₂ uptake by a leaf is just balanced by respiratory CO₂ release) is CO₂-dependent in C₃ species, but not in C₄ species (Ehleringer and Bjorkman, 1977). High CO₂ increases C₃ species' quantum yield because it suppresses photorespiration. The extent to which the effect on quantum yield manifests itself as plant growth is dependent on the second pertinent aspecthow whole plant (dark) respiration responds. If whole plant respiration is less under high CO₂, then the light compensation point is lowered and some growth is achieved at light intensities that otherwise would prove insufficient for growth to occur. The larger relative enhancement of growth in wheat (reported to show reduced whole-plant respiration in high CO₂) in low light compared to high light intensities might be explained on this basis. For other species such as soybean, which has shown increased respiration under high CO₂, the relative enhancement of growth by high CO₂ appears equal at low and high light (Sionit et al., 1982).

With the prospect of warmer average global temperatures in the future, the response of CO₂-enriched plants under higher temperatures is pertinent. Based on limited information, it appears that in general the positive effect of higher CO₂ in stimulating photosynthesis is increased with higher temperature. However, this effect tends to be counteracted by negative feedback effects over the growth cycle of the plant. For example, for two C₃ species, Berry and Raison (1981) found that the ratio of short-term leaf photosynthesis at 1000 ppmv to that at 330 ppmv CO₂ increased sharply from 1.15 at 15 °C to 3.5 at 50 °C. This is explicable on the basis of the kinetic properties of rubisco and perhaps also on the declining solubility of CO₂ (relative to O₂) with increasing temperature (Jordan and Ogren 1984). However, temperature is important in determining the rate of growth of metabolic sinks (such as developing fruits). Sufficiently high temperatures can adversely affect sink growth (see also Section 9.3.1) and thereby feed back onto leaf photosynthesis and modulate the CO₂ response. This could possibly be one reason why soybeans, grown at supra-optimal temperature (above 30 °C) did not express the large potential CO₂ responsiveness of photosynthesis in enhanced growth (Hofstra and Hesketh, 1975; Hofstra, 1984).

At very low temperatures the inherent capability of sinks to grow is low and not limited by photosynthate supply. Thus even if photosynthesis were highly responsive to CO₂ enrichment at low temperature, it might not have much effect on sink growth that is itself temperature limited. However, high CO₂ can reduce the minimum temperature at which a plant grows and completes its life-cycle. The tropical vegetable okra (Abelmoschos esculentor) was unable to complete its life-cycle in normal CO₂ at temperature below 23 (day)/17 °C (night), while plants grown in 1000 ppmv CO₂ at 20/14 °C matured and produced fruit (Sionit et al., 1981b). Thus there was an infinite relative response (i.e. from nothing to something) of fruit yield to high CO₂ at sub-critical temperatures.

Response of plant growth to high CO₂ under nutrient deficiency or surfeit varies with both the nutrient and the species concerned. Low nitrogen supply reduces growth of all species, but with C₃ non-legumes, DW growth of both N-deficient and N-sufficient plants is increased by doubling normal CO₂ concentration. For instance, the weight of cotton plants almost doubled, irrespective of whether they had received 2 mM or 24 mM nitrate in the nutrient solution (Wong, 1979). Although not as pronounced, perennial ryegrass, wheat and soybean also achieved high per cent increases of dry weight growth from CO₂ enrichment under N-deficiency (Sionit et al., 1981a; Goudriaan and de Ruiter, 1983). The implied improvement in N-use efficiency may emanate from reduced investment in photosynthetic machinery (which has a high N-requirement) per unit of photosynthetic assimilate produced. In nodulated legumes such as soybeans or peas, high CO₂ leads to greater biological nitrogen fixation (Hardy and Havelka, 1974). This effect can be attributed to the production of more nodules on a bigger root system, rather than to greater specific activity of nodules (Phillips et al., 1976; Finn and Brun, 1982). In short, the CO₂ effect is positive under nitrogen stress.

In contrast, Goudriaan and de Ruiter (1983) were unable to show a growth response to CO₂ in phosphorus deficient plants of several species (with the exception of P-deficient bean (Vicia faba) plants which were even more responsive to high CO₂ than were plants grown with adequate P).

Potassium is another major nutrient but there is little information on its interaction with atmospheric CO₂. In potato, Goudriaan and de Ruiter (1983) noted a negative effect of increased CO₂, probably associated with higher demand for potassium.

Sodium is an essential element for C₄ photosynthesis. Growth of sodium deficient plants of two C₄ species which do not normally respond to CO₂ enrichment was greatly enhanced by high atmospheric CO₂ (1500 ppmv) (Johnston et al., 1984). While sodium deficiency is uncommon in the field, sodium excess (salinity) is common and causes reduced yield or, at greater excess, toxicity symptoms. Schwarz and Gale (1984) have shown that for diverse species, tolerance of saline conditions is increased by CO₂ enrichment to 2500 ppmv. This effect was ascribed to a shortage of photosynthate in plants suffering salt stress, but it might also be associated with the reduced demand for saline water because of CO₂-reduced transpiration.

9.2.5 Summary

Based on limited experimental results, we can expect a doubling of atmospheric CO₂ concentration from 340 to 680 ppmv to cause a 0 to 10% increase in growth and yield of C₄ crops (such as maize and sugarcane) and a 10 to 50% increase for C₃ crops (such as wheat, soybean and rice), depending on the specific crop and prevailing growing conditions. For C₃ species, the principal source of this response is at the level of the primary carboxylaseoxygenase enzyme, but stomatal, respiratory and morphological responses may also be involved. The latter three effects appear to be the principal sources of growth response for C₄ plants, where response occurs.

Table 9.2   Effects of increased CO₂ on crop response and feedbacks: a tentative compilation

	C3	C4
Photosynthesis	+ +	0
Photorespiration		NA
Nitrogen fixation	+	NA
(leguminous species)
Transpiration
Dark respiration	M	?
Leaf area development	+?	+?
(non-photosynthetic
CO2 response)
Photosynthetic acclimation	M	M
(at leaf level and via sink:source ratio)
Senescence	M	0?
Leaf area expansion	0 to +	0
(via greater photo synthesis)
+ + = strongly positive
+ = positive
= negative
0 = no effect
NA = not applicable
M = mixed response (positive or negative)
? = not known or uncertain

There are numerous feedbacks operating within the plant that serve both to accentuate and to attenuate the effect of the primary responses. An important positive feedback is the increase in the proportion of incident radiation that is intercepted by leaves because of more rapid leaf expansion in a stand of CO₂ enriched plants. Whereas in C₃ plants this stimulation of leaf expansion is likely to result mainly from the CO₂-stimulated growth itself, in C₄ species circumstantial evidence suggests that it may be a non-photosynthetic effect of higher CO₂. Important negative feedbacks can develop from the build-up of photosynthetic products on the leaves and from changes in the source:sink ratio of the plant. The effects of CO₂ enrichment on the basic biochemical and physiological plant processes and feedbacks are summarized in Table 9.2.

Table 9.3 Relative effects of increased CO₂ on growth and yield: a tentative compilation¹

Interactions between the effects of atmospheric CO₂ and other growthlimiting environmental variables on plant growth are complex and not amenable to simple generalization from the so-called 'law of limiting factors'. For example, high CO₂ concentration can reduce the deleterious impacts on growth of water-shortages, low light intensity, temperature extremes or certain mineral deficiencies, notably nitrogen deficiency. On balance, the results of experimental studies indicate that in most conceivable circumstances, the effects of increased CO₂ are beneficial (rarely detrimental) to plant growth and yield, as indicated in Table 9.3.

On the other hand, the scientist's glass-house is not the same as nature's laboratory. Carbon dioxide enrichment also stimulates the growth of weeds, for instance, which compete with crops for available moisture, light, and nutrients in actual field situations. Field studies of CO₂ enrichment have been attempted (e.g. Rogers et al., 1981), but suffer from poorer control of environmental conditions than can be obtained in growth chambers, and have not yet progressed to the stage of studies on competition in mixed plant communities. One important goal of experimental work is to contribute to the development of simulation models (discussed below). In lieu of field studies such models allow one to examine how the biochemical, physiological and environmental factors interact dynamically in the presence of high CO₂ to influence plant growth and yield. Some models have been used in this regard (e.g. Baker and Lambert, 1980; Goudriaan et al., 1984) but greater progress in model development is required before we can place high confidence (at, say, the 5% level) in the results. Such progress is being made.  

9.3 PERSPECTIVES ON CLIMATE IMPACTS

How is agriculture affected by changes in climate in the absence of direct CO₂ effects? There are myriad answers: crop varieties are switched, cultivation techniques are modified, plant development is retarded or accelerated, yields become more or less variable, production trends are altered, cropped area expands or contracts, and so on. Ideally, we should like to know the aggregate of these effects, but in the short term this is impracticable. We are forced to be selective.

The selection of research questions depends, in large part, on one's perspective concerning the effects of climate and climatic variation on agriculture. The perspectives vary widely. For instance, Bach et al. (1981) expand upon two related but opposing themes: climate as a `resource' and as a 'hazard'. Riebsame (1985) distinguishes two additional perspectives: climate as 'setting' (the background for agriculture) and climate as `determinant' (the cause of agricultural patterns and practices). Glantz (1979) identifies yet another dichotomy: CO₂-induced climatic change as an `event' (a focus on the doubling) and as a 'process' (a focus on the gradual, accumulating environmental change). There is considerable overlap between these perspectives.

Two additional perspectives can be discerned from the rapidly mounting literature on the agricultural effects of climatic change. One view holds that the potential problems (or benefits) for agriculture arise from slow, gradual changes in average climate. The other view portrays the problem as one of slow shifts in climatic risks. The way in which agricultural impacts of climatic change are assessed takes a slightly different twist according to which view predominates. Let us characterize them. 

9.3.1 The Slow Change View 

The 'slow change' view is implicit in most impact studies. It derives, in part, from the way in which the entire problem of increasing greenhouse gases and climatic change has been analysed. As reflected in this volume, the analysis begins with estimates of past and future emissions of greenhouse gases, followed by estimates of their rates of accumulation in the atmosphere and by predictions of their effects on climate. In general, the changes in climate variables are presented in terms of their central tendencies, based on the equilibrium response of climate models. It is not unexpected to find an extension of this chain of analyses to assess the impacts on agriculture and other ecosystems. The slow changes in average temperature or precipitation predicted in the previous step are quite literally assumed to be the potential problem faced by agriculturalists: a gradual, long-term, cumulative alteration of climate and, consequently, a slow deterioration (or enhancement) of the growing environment.

The specific research questions derived from this slow change perspective are cast in a similar mould. For example, 

• How would average wheat yields be affected by a 2 ^°C rise in average annual temperature? 

• How might the boundaries of North American corn production slowly recede from its present semi-arid margins with a gradual regional drying and warming?

• In the light of growing global food demands, how might world-regional grain production trends and trade balances be affected by gradual climatic changes?

• How would (or could) agriculturalists perceive and adapt to the slow changes in their environment?

This last question concerning response is particularly vexing from the slow change view. On the one hand, it has been argued that the magnitudes of some estimated changes in climate are so large that they are unprecedented in recorded history and thereby fall outside the realm of human experience. In order to adapt, agriculture may have to devise wholly unique and imaginative strategies for dealing with the effects (Cooper, 1978; 1982). On the other hand, it has also been argued that because the climatic changes will occur in a slow, cumulative fashion, agriculture has plenty of time and can most likely adapt in pace (e.g. Wittwer, 1980). The rate of change should be slow enough to allow farmers to perceive the changes in their growing environment and to switch crops, to adopt more suitable varieties, and to modify their farming practices accordingly. In short, the transition, while formidable, is eased by the luxury of time. These appraisals hinge upon assumptions concerning adaptive capacity and rates of response, to which we shall return shortly.

9.3.2 The Shift-in-risk View

The CO₂ problem as a shift in climatic risks presents an interesting contrast. According to this view, the potential agricultural problems arise mainly from changes in the frequencies of unusually disruptive (or beneficial) climatic events. There is no denial that changes in regional climates may occur slowly and gradually. It is argued, however, that the long-term changes in average temperature or precipitation per se are of relatively little importance to agriculturalists. Rather, the year-to-year risks from climatic events such as droughts, frosts, or excessive moisture are more important (e.g. Fukui, 1979). The impacts of these relatively infrequent events on crop yields cause financial (or other social, economic or human) stress and play a large role in determining agricultural viability. With a change in climate, it is likely that the frequencies of such events would shift.

In part, this 'shift-in-risk' view is a result of reversing the chain of analyses described earlier, that is, by commencing with agriculture and working toward changes in climate resulting from increasing concentrations of greenhouse gases. The questions are posed: What are the processes or resources (climatic or otherwise) critical to agricultural activities and yields? How might changes in climate affect these processes or resources? Parry and Carter (1984) call this an 'adjoint approach'. This approach, incidentally, creates greater complexity and uncertainty in climate analysis and places the onus on the climate modeller to provide more impactspecific climatological detail at relevant scales of resolution-hence the existing gap between needed and available information for impact studies (WMO, 1984).

The shift-in-risk view leads one to formulate specific research questions rather differently. For example, 

• What climatic conditions lead to particularly poor yields of sorghum and millet in the semi-arid sub-Saharan tropics? How would the probabilities of their occurrence shift with a change in climate? 

• How would the boundaries of the wheat belt in the drought-prone margins of North America be altered from increased drought risk that may result from a warmer and drier climate? 

• What would be the impacts on global grain production from simultaneous poor harvests in North America and the Soviet Union? How might the frequencies of such occurrences change if the climate of the Northern Hemisphere were to change? 

In these questions, the emphasis falls on the interannual variability of climate. 

The focus on year-to-year events rather than long-term means is partly grounded in assumptions concerning agriculturalists' perception of climatic change. In the absence of scientific information, agriculturalists may encounter considerable difficulty in perceiving and reacting to changes in mean trends. Indeed, atmospheric scientists themselves can only identify a trend in climate if a sufficiently long period of record is available to separate the `signal' from the `noise'. Instead, in the face of changing climate, the impacts from the occurrence of particularly unfavourable (or favourable) growing seasons are, in effect, the principal stimuli to which agriculturalists can, and do, react (as in changing crop type or variety, migrating elsewhere or adopting different technologies or cultivation techniques).

Thus, from the shift-in-risk view the environmental cues to which agriculture will respond are not unprecedented (even though the magnitude of the climatic change itself may be unprecedented). The cues are the same year-to-year events that agriculturalists now experience, albeit at different frequencies of occurrence. Therefore, many tactics and strategies already exist for dealing with these familiar climatic risks, although different levels of adoption, changes in farm structure and organization, or possibly new risk strategies may be warranted. This viewpoint is implicit, for example, in the remarks by Clark (1982) who states that `...what we would be doing if we were certain about CO₂ predictions is what we should be doing anyway to cope with droughts, heat waves, etc.' (p. 3).

9.3.3 Blending the Views: Adaptation and Adjustment 

While we have dichotomized the slow change view and the shift-in-risk view for purposes of explication, they are not mutually exclusive. This is evident in many statistical crop impact studies (Section 9.4.1) in which the effects of climatic extremes are reflected in mean yields; the mean and variance are inextricably bound in their long-term effects on yield trends (Mearns et al., 1984).

Furthermore, it has been hypothesized that long-term adaptation to climate results from the aggregate of short-term responses to risk (Parry, 1978, 1985; Whyte, 1981; de Vries, 1980). As agriculturalists strive to achieve the best returns and to build resiliency in the face of interannual climatic variability, the agricultural system becomes best fitted to the most frequently occurring climatic conditions over the long runthat is, those described by measures of central tendency. For example, both the heart and the spatial extent of the Australian wheat growing regions may be largely a reflection of the spatial gradients of drought risk, but may be well described by mean rainfall.

In this respect, the two views just simply may be emphasizing two separate bands of the same response spectrum. We illustrate this notion in Figure 9.3, partly adapted from Fukui (1979). Figure 9.3a displays a hypothetical curve of precipitation, conveniently distributed normally (in reality precipitation is usually described better by non-Gaussian curves). Let us assume that an agricultural system is perfectly centred on the mean, so that any deviation from the mean has negative effects on yields. Superimposed on the distribution in Figure 9.3b are three categories of agricultural response (after Burton et al., 1978; Kates et al., 1985).

First, for frequently occurring amounts of precipitation there exists a set of responses that are labelled adaptations. From one year to the next, agriculturalists expect mildly wet or dry conditions to occur. It is this expectation of weather that, to the agriculturalist, is `climate' (Hare, 1985). Individuals and organizations accumulate a large mix of cultural, technological or behavioural measuresadaptationsto accommodate this expected variation. They are reflected broadly in the timing of farm operations like planting and harvesting, the migration routes of pastoral nomads, or the spatial patterns of major agricultural systems like livestock grazing or dryland wheat farming. Adaptations evolve over the long term (greater than several generations) and may not be consciously recognized as having any relationship at all to climatic or environmental fluctuation. Adaptations allow agriculture to interact freely with the expected environment without disruption or inhibiting stress. Within this `band' of adaptation, climate is a resource.

Figure 9.3 Schema of adaptation and adjustment to climate and climatic change (adapted partly from Fukui, 1979). (a) Frequency distribution of a climatic element, upon which are superimposed 'bands' of adaptation and adjustment (b). (c) A change of mean (X₀ to X₁) requires a shift in adaptation (A₀ to A₁) and adjustment (B₀ to B₁) in order to compensate for higher frequencies of dry (hatched) and extreme drought (cross-hatched) events

Third, at the far tails of the distribution are the very extreme, rare events-for example the 1-in-500 year flood or drought. Few specific adjustments are contemplated, either because they would be too costly, no viable alternatives are perceived, the events are considered too rare, or some combination thereof. Herein lurks the potential for catastrophe: a decade of drought in the North China Plains or five consecutive years of monsoon failure in South Asia.

In this sense, the future impacts on crop yields and production depend on the dynamics of agriculture and society as well as the stimulus of environment. For example, it has been asserted (Burton et al., 1978) that, in many developing countries struggling with transition to modern agriculture, the bands have shrunk rapidly. Traditional adjustments and adaptations have been displaced or discarded, while the more technological or market-oriented mechanisms that are characteristic of the developed world have not, as yet, been satisfactorily adopted. This creates situations of high vulnerability to the vagaries of climate, as evidenced by the high tolls exacted by the occurrence of extreme climatic events (Kates, 1980).

What are the effects of climatic change? In Figure 9.3c we have superimposed a change in climate, a slow change in mean from ₀ to ₁-drier conditions-assuming no change in variability. For conditions described by the new mean (₁) and the expected deviations immediately around it, the change results in lower crop yields more often than before. But these yield changes fall well within the existing band of adaptation; so, on a year-to-year basis they are not really unexpected or disruptive, and there is no lack of mechanisms to accommodate them. The potential problem is rather to recentre on the central tendency over the long run. The flexibility afforded by the adaptive capacity of the agricultural system will most likely allow it to calibrate fairly easily, closely in pace with the changing climate, as through gradual alterations of planting dates, planting densities, or allocations of irrigation watera fine tuning of the system.

Greater difficulties are encountered further left on the curve in Figure 9.3c. Climatic events that were once infrequent enough to be considered hazards (i.e. moderate droughts) now occur with troublesome regularity. Agriculturalists may begin to perceive them as part of the expected weather. If agriculturalists wish to maintain the levels of acceptable risk previously attained, the higher probability of loss or disruption associated with these events (represented by the hatched area) becomes intolerable. In effect, agriculture is under-adapted. An expansion of the band of adaptation from A₀ to A₁ will be required. This might be accomplished, for example, through a continuous adoption of stress-tolerant crop varieties, an expansion of farm sizes, or a switch to diversified farm operations that are more suitable to the new climatic conditionsan alteration of the system.

At the far left tail of the curve in Figure 9.3c are the extreme droughts to which agriculture is largely unadjusted. By virtue of the climatic change, the occurrence of these events has become more probable. Again, if previous levels of acceptable risk are to be maintained, the band of adjustment must be expanded accordingly, from B₀ to B₁ (the cross-hatched area in Figure 9.3c). But, in many cases, the alternatives may be so severely limited or prohibitively costly, and the impacts so disruptive in terms of crop yields and socio-economic consequences, that the only perceived recourse may be abandonment and migration. The Dust Bowl migrations from the U.S. Great Plains during the 1930s (Worster, 1979), the abandonment of cereal and hay production in Iceland with the Little Ice Age (Ogilvie, 1981), or, perhaps, the present situation in vast areas of drought-stricken Africa are illustrative. The long-term effect could be a change in land use and agricultural landscapea change of system.

Figure 9.4 The sensitivity of extreme climatic events to changes in the mean, based on normal distribution and constant standard deviation (see text for explanation) (from Wigley, 1985)

Of course, on the right 'wet'side of the curve the frequencies of occurrence have been reduced. For these events it could be argued that agriculture is over-adapted and over-adjusted in relation to apparent levels of acceptable risk. 

Three additional points should be emphasized with respect to climatic change and risk. First, the frequency of occurrence of extreme events can be very sensitive to relatively small changes in the mean (Mearns et al., 1984; Wigley, 1985). This relationship is illustrated in Figure 9.4 (from Wigley, 1985). The abscissa shows the change in the mean (X) as a multiple of the standard deviation (S), while the ordinate shows the resulting change in the probability of extreme events with initial probabilities (PI) of 0.1, 0.05, and 0.01 (like the previous figure, this diagram is based on an assumed normal distribution and constant standard deviation). For example, if the annual mean precipitation over England and Wales (approximately 920 mm) fell by 100 mm (approximately 0.9 standard deviationsan amount, by the way, projected by some GCMs with a CO₂ doubling), the initial 1-in-100 year drought (P1 = 0.01) would become roughly 7.5 times more frequent in any given growing season (P2/P1 = 7.5, point A).

Second, as pointed out by Parry (1985; also see Sakamoto et al., 1980), individual farmers and agricultural systems may be especially vulnerable to consecutive years of poor yields, and the probabilities of consecutive occurrences of extreme climatic events could increase dramatically with a change in climate. For instance, while in the previous example the initial 1-in-100 year drought became 7.5 times more frequent, the chances of two consecutive years of drought of this magnitude would increase by over 56 times (assuming independent events). The potential for a catastrophic succession of poor harvests, particularly in areas already sensitive to drought, could escalate rapidly, even if the change in climate itself (as measured by the central tendency) were glacially slow.

Third, it is likely that the agricultural response would not be smooth and gradual. The disruptive climatic events are already infrequent, so considerable time might pass before farmers could perceive that the probabilities were changing. In the absence of credible scientific information, response would come about through direct experience, as through a rash of particularly severe years of unfavourable weather (if, indeed, climatic change is for the worse). In this way, agricultural response is apt to occur in an abrupt, step-like manner as human perception catches up with physical reality. In the meantime, the adverse impacts could be severe.

We have attempted to show that the slow change view and the shift-in-risk view just simply emphasize different aspects of the same problem of climatic change. The climatic effects of increased concentrations of greenhouse gases, although commonly described in terms of long-term, large-scale averages, can be manifested in many ways across a wide range of spatial and temporal scales. In the global context, one danger is that the problem of climatic change may be defined too narrowly. For instance, people who represent the interests of developing countries sometimes claim that the problem of a slow, long-term change in climate is quite secondary to immediate problems of interannual yield variability, and is therefore of limited interest (WMO, 1984). This is unquestionably a valid point from the slow change view. However, from the shift-in-risk perspective the potential agricultural impacts of climatic change could be interpreted as an exacerbation of existing yield variabilitya problem which could be felt acutely, abruptly, and possibly in the not-so-distant future. 

9.4 THE IMPACTS OF CLIMATIC CHANGE 

Four broad approaches to assessing the agricultural impacts of climatic change can be identified. Crop impact analysis concentrates directly on estimating the primary effects of environmental variables on crop yields. Marginal-spatial analysis examines the possible spatial shifts in cropping patterns (or other characteristics of agriculture) that might result from changes in climate at the margins of production. The third approach, agricultural sector analysis, focuses on estimating the range of impacts within and between agricultural regions, with an emphasis on the positive and negative feedback mechanisms that, in a dynamic fashion, reduce or enhance the primary impacts on crop yields and production. Finally, historical case studies ask, What does past experience tell us about the agricultural impacts of climatic change? Let us examine each approach.

Figure 9.5 Crop impact analysis. The approach is largely unidirectional and sequential and seeks to estimate the primary, first-order impacts of changes in the growing environment on crop responses and yields as a result of increasing atmospheric concentrations of greenhouse gases

9.4.1 Crop Impact Analysis

 The first approach is presented schematically in Figure 9.5. Crop impact analyses seem to isolate and to quantify the effects of climate variables (including the direct CO₂ effects, treated separately in Section 9.2) on crop response and yields. In applications to problems of climatic change, such analyses have attempted to estimate the 'before-and -after' yield effects, usually assuming an instantaneous change from one climate state to another. Although frequently unstated, rather constrictive boundary conditions are required, and the results of most crop impact analyses should include the following caveats:

• If no shifts in spatial cropping patterns take place to adapt to changes in regional climate.

• If no changes in perception and managerial response occur.

• If the technologies and cultivation practices that affect crop-climate relationships remain constant over time.

•  If no feedbacks to yields and production from market forces or government policies occur. 

Of course, these are big `ifs', and, as we shall see, subsequent approaches (Sections 9.4.2, 9.4.3 and 9.4.4) progressively relax these constraints by setting the boundary conditions to include wider aspects of the problem. 

Crop-climate models 

Most crop impact analyses have relied on three methods for assessing the possible effects of climatic change, each of which has its advantages and drawbacks. In empirical-statistical, multiple regression models, some aspect of productionusually commercial yieldsis explained by some set of 'independent' climate variables, like monthly values of precipitation and temperature, plus a term to account for any long-term trends in yields that are usually attributed to 'technology'. The constants in the regression equation are determined empirically, and the observations for regression fitting are taken from historical records of agricultural production and climate data. The more explanatory variables included in the regression equation, the larger the number of empirically derived constants. This, in turn, requires a long historical record to provide a sufficient number of observations to derive statistically significant equations and avoid spurious resultsa major constraint in many countries where reliable historical records are short. Even where records are sufficiently long, changes in crop varieties, management or technology can alter cropweather relationships and, in effect, make historical data 'outdated' (Robertson, 1983). This is a serious drawback to using such models to predict the long-term effects of changes in climate on yields.

Regression techniques are not particularly suitable for understanding the interacting physical, biochemical and physiological processes underlying crop growth and yield. They skip the stage marked `plant response' in Figure 9.5 and attempt a direct link between environmental change and reported yield. This is the 'black-box' criticism frequently levelled at regression models (e.g. see Katz, 1977). Furthermore, differences in crop varieties, management practices and soil conditions are difficult to include as explanatory variables in regression equations (this would also increase the number of constants). Thus regression models tend to be site specific, and it is commonly accepted (but frequently ignored) that they should not be applied outside the region or data range from which they were constructed.

With the advent of computers, it has been possible to construct crop-growth simulation models which combine the mathematical equations that describe the physical, chemical and physiological mechanisms and their interaction. Such models focus explicitly on plant processes such as photosynthesis, transpiration and respiration. Data requirements for simulation models are, as a rule, demanding. The simulation time-step can vary from weeks to minuteshourly is commonand data on radiation, minimum and maximum temperatures, and soil moisture are required at those same time intervals.

One major advantage of simulation models for assessing the impacts of climatic change is their potential 'transportability'. In principle, if the processes of plant growth are described accurately and integrated correctly, the specific region of application should be of little consequence, since the model itself will demonstrate the limiting factors for growth (Baler, 1977). The effects of different management practices or environmental sensitivity can then be examined systematically.

With assumptions about management, soil conditions and planting densities, area-wide yields can be estimated using simulation models. However, Monteith suggests (WMO, 1985) that, despite their complexity and processorientation, computer simulations have not been conspicuously more successful than simpler models in making predictions of crop yields. In fact, attempts to be comprehensive have sometimes increased the size and complexity of models to the point where confusion eclipses illumination.

Intermediate to the regression and simulation approaches are simpler, deterministic mathematical functionsor mechanistic schemes (cf WMO, 1985)that relate individual climate variables to particular crop growth processes over the stages of plant development. Such schemes are especially useful for analysing the effects of a specific climate variable with respect, say, to its limiting or optimal conditions. However, their simplicity contributes to their principal drawback: the failure to consider the correlation and interaction of elements, the adaptation of plants to stress over the period of growth, and the growth restrictions imposed by nutrient deficiencies, pests or other factors (Monteith, 1981). In short, mechanistic schemes lack comprehensiveness and dynamismthe fundamental rationale for building simulation models. Mechanistic schemes provide the building blocks for process-based simulation models. ⁵ 

The strengths and weaknesses of crop-climate models are summarized in Table 9.4.⁶ In general, a common deficiency of all three types of model is the lack of rigorous validations. Ultimately, all models should be tested on independent data (not used in model construction or parameter estimation), a criterion which applies to 'process-based' simulation models and mechanistic schemes, as well as to regression models (Robertson, 1983; Haun, 1983). It is likely that many models that are potentially useful for crop impact analysis in various regions of the world have not been adequately validated, although the extent of the situation has yet to be determined (WMO, 1985).

Despite their deficiencies, crop-climate models have been used to examine the possible impacts of climatic change. What have we learned?

⁵  it is instructive to note that mechanistic schemes are the outcome of laboratory and field measurements of plant processes fitted to mathematical functions based on the laws of physics and physical chemistry. As such, they contain statistical summaries of experimental workand, thus, so do simulation models. Therefore, although we make the distinctions between statistical regression models, mechanistic schemes and crop-growth simulation models, the distinctions are somewhat artificial.

 ⁶  For reviews of crop-climate models, see WMO (1982; 1985), Baier (1977; 1983), Robertson (1983), Biswas (1980), CIAP (1975), Sirotenko (1983), or Nix (1985).

 Table 9.4   The (a) uses and (b) criticisms of types of crop-climate models: statistical relations (SR), mechanistic schemes (MS) and crop-growth computer simulations (CS) (after WMO, 1985)

Applications and Findings

Crop impact analyses that deal explicitly with the subject of climatic change are surprisingly few in number. Most studies have concentrated on wheat and maize in the mid-latitudes. The major studies of wheat and maize, plus a few pertinent analyses of lesser scope, are listed in Table 9.5. It is evident that a heavy emphasis has been placed on North America and Europe, as reflected in the recent undertakings by the U.S. National Academy of Sciences (NRC, 1983) and by the European Economic Community (Meinl and Bach et al., 1984), and, for the most part, in the decade-old Climate Impact Assessment Program study (CLAP, 1975). The only attempt at systematic, global crop impact analysis was the National Defense University study (NDU, 1980); but in lieu of modelling, the NDU study opted for a consensus of 'expert judgment' regarding climateyield relationshipsa tactic that has drawn heavy criticism (for recent critiques see Stewart and Glantz, 1985; Schneider, 1985. Only the American crop impact analyses are noted in Table 9.5).

All the studies in Table 9.5 implicitly assumed a `slow change' view of the problem of climatic change. They investigated the changes in average yields accompanying changes in long-term climate. To perturb yields, most of the studies used arbitrary scenarios (see Section 8.3) in which changes in climate were imposed instantaneously and uniformly across seasons; instrumental analogues and GCM scenarios were used in a few cases. In short, the list of crop impact analyses contains a rather diverse mix of methods, with respect both to choice of cropclimate model and to assumptions about climatic change.

Although this diversity should deter all but the most foolhardy from comparing the findings, we have attempted to do so in Figures 9.6a-f. The symbols plotted on the graphs correspond to those noted in Table 9.5 (the 'hollow' symbols represent regression, the `solid' are simulation and the 'letters' denote mechanistic or other methods). The graphs show the average yield changes expected with specified changes in precipitation and temperature. The general conclusion one can draw from the patterns in Figure 9.6 is that despite the diversity of scenarios and methodsregression, simulation, mechanistic, and even expert judgment, with all their inherent deficienciesthe studies are in basic agreement regarding the expected direction of yield effects in current cultivars of wheat and maize from changes in climate. They are less precise about the relative magnitude of those effects. Two basic observations can be made.

 First, warming appears detrimental to yields of wheat and maize in the core crop regions in the mid-latitudes of North America and Europe. (Effects at the cold margins of production or other specific locations might be quite different, as discussed in Section 9.4.2). Keeping in mind the large uncertainties in these findings, we may venture a rough guess at the magnitude of impact: with no change in precipitation (or radiation), slight warming (+1 ^°C) might decrease average yields by about 5 ± 4%; a 2 °C might reduce average yields by about 10 ± 7%. To put this into perspective, at current (1983) levels of production, a 10% decrease in wheat and maize yields in North America is equivalent to about 20.5 mt, or 10% of global trade in cereals (FAO, 1983a,b).

Table 9.5 Studies of crop yield impacts from climatic change, with particular reference to wheat and maize

Figure 9.6 Estimates of the impacts on wheat and maize yields of temperature and precipitation changes for given cultivars (for explanation of symbols, see Table 9.5)

Second, reduced amounts of precipitation also decrease yields of wheat and maize in these same core regions. This implies, of course, that both higher precipitation and higher temperatures could have offsetting effects on yields, as indicated by the discernible negative slopes to the data plotted in Figure 9.6. Temperature changes could be expected to have broadly uniform effects on yields over large areas, as compared to precipitation changes whose yield effects would vary over shorter distances depending on local rainfall regimes and soil conditions.

It would be incautious to conclude anything more specific from the results of these studies. 

The physical reasons for the reductions in yield with increasing temperature (even in cool regions like Canada or the United Kingdom) are provided by the process-based simulation models and the mechanistic schemes upon which they are based. There are two processes at work. First, higher temperatures are usually associated with higher evapotranspiration and therefore greater moisture stress during critical stages of growth. This effect is likely to be important in regions where inadequate soil moisture is already a characteristic problem.

Second, temperature influences the duration of the growth period of the plant. Yield depends on the rate of photosynthesis (determined principally by available CO₂, light, water and, to a lesser extent, temperature) and the duration of the growth period, which is largely a function of genotype and temperature. Higher temperatures stimulate photosynthesis slightly (which can be beneficial, particularly during emergence and canopy formation) but, to the detriment of yields, accelerate development and shorten the duration of plant growth (an effect that can readily be countered by a switch of cultivar). The latter phenomenon is particularly important during the phenological stages of heading, flowering and ripening in which the economic portion of dry weight yield is formed⁷.

Based on such reasoning, Monteith (1981) simply calculated that, assuming a base temperature of 0 °C for growth and a mean temperature of 15 °C over the growth period, a 1 °C increase in mean temperature would decrease yield by 1/15, or by about 7%. His a priori reasoning is in corroboration with the correlation between observed average wheat yields and mean growth-period temperature throughout England (Figure 9.7). In the same way, Goudriaan (WMO, 1985) attempted a rough estimate of the range of possible crop impacts, assuming that temperatures could increase from l ^°C to 4 °C and that precipitation and radiation (from cloudiness) could change 10% in either direction (with relative effects on yields of the same order of magnitude) with a doubling of atmospheric CO₂ concentrations. Ignoring the direct effects of CO₂ and the possibility of change of cultivars, the net influence of climatic change on yields could range from + 13 to 47%, depending upon whether temperature, radiation and precipitation counteract one another or work in the same direction. (For comparison, we excluded radiation effects and plotted the resultant range, +3 to 37%, in Figure 9.4). Monteith's and Goudriaan's rough estimates are not inconsistent with the estimates derived from more complicated techniques.

⁷  The empirically derived coefficients in the wheat and maize regression models used by Waggoner for the NRC study (1983) are generally consistent with these relationships.

Figure 9.7 Mean yield of wheat in England (19561970) as a function of mean temperature for May, June and July for the period 1941-1970. Records for Cornwall, Devon and Surrey (open circles) were omitted from the analysis. Line of best fit has slope of -5% K^-1 at 14 °C (from Monteith, 1981)

What do these analyses tell us about the possible 'shifts in risk' as a result of climatic change? Explicitly, very little. The decreases in average yields from higher temperature, for example, might well be more than compensated by a concomitant reduction in the risk of damaging early- or late-season frosts-a troublesome aspect of cereal production in cooler climates (Lough et al., 1983). Drier conditions would not only reduce average harvests, but could also inflate the frequency of harvest 'failures' due to extreme drought-a double burden in semi-arid regions. Implicitly, these effects are reflected in average yield changes generated by regression models, since the regression coefficients capture some of the yield effects of extreme climatic events as they are correlated with average monthly temperatures or precipitation (Mearns et al., 1984). But one cannot glean much regarding their specific yield impact or about changes in their frequency of occurrence. 

Several studies have used crop-climate models simply to obtain estimates of yield impacts due to interannual climate variability or anomalous growing seasons. For example, NOAA (1973), TIE (1976) and Warrick (1984) examined impacts on grain production in North America. The latter study, for instance, employed a set of regression models (one of which was essentially the same as that used by Waggoner, 1983, for the NRC study) to investigate the possible impacts on Great Plains wheat yields if the drought years of the 1930s were to recur. It was found that the worst drought year could reduce Plains-wide yields by about 25%. However, while these studies examined yield impacts of anomalous years, they did not make the link to long-term climatic change and thus did not consider possible changes in the frequencies of large yield departures.⁸

An important exception to this last point is the study by Waggoner (1983). Waggoner imposed an arbitrary scenario of climatic change (l °C warmer, 10% drier) upon an historical climate record (without regard to seasonal or spatial considerations) and, using a crop-growth model, simulated wheat yields (in North Dakota) year by year. He thus derived frequency distributions of yields with and without climatic change, as shown in Figure 9.8. (In contrast, all other studies noted in Table 9.5 simply used mean values of climate variables to generate average yield values.) Waggoner only concerned himself with the change in median yields, but the projected shifts in the frequencies of poor and bumper harvests from his simulation are far more interesting. Under unperturbed climate, the chance of getting extremely low yields, 24% or less below expected median yield, was approximately l-in-8 years. Under the warmerdrier scenario (and ignoring direct CO₂ effects), the risk of obtaining these same absolute yield levels jumped to about l -in-2.3 years, a relative increase in risk of over 300%. At the other tail of the distribution, the chances of unusually high yields were drastically reduced. Whether North Dakota wheat growers could continue to prosper under such shifts in risk is questionable.

The importance of this study lies not so much in the predicted yield values themselves (there is room for considerable scepticism in this respect), but in the approach. By simulating the frequency distributions of yields, the spectrum of yield effects, including the risks of extreme events, can be examined. In terms of evaluating possible adjustment strategies or policy changes, such analyses are far more useful than simply estimating average yields. In general, the value of crop impact analyses could be enhanced by following similar procedures.

⁸  Neild et al. (1979) did explicitly consider possible shifts in risk, but in terms of climatic events, not yields. One finding of the study was that one type of climatic warming, a lengthening of the series of consecutive days above normal, actually increased the incidence of spring freeze damage on early-planted maize in the northern U.S. Corn Belt. The apparent reason is that warmer temperatures promote earlier emergence, thus lengthening the period of exposure to infrequent freezes.

Figure 9.8 North Dakota simulated spring wheat yield (1949-1980) (from Waggoner, 1983)

Global Assessments and Prospects

What can be said about the possible impacts of climatic change on crops other than those grown in the cereal regions of the mid-latitudes? Unfortunately, few studies have been conducted on crops in other parts of the world, particularly in the tropics and sub-tropics. Crop-climate models have been developed world-wide and could be useful in addressing issues related to climatic change. A survey undertaken by WMO (in conjunction with this current CO₂ assessment project) revealed 49 models in 28 countries (of WMO member countries) which potentially could be applied.

It is unrealistic to believe, however, that any crop-climate model can predict the long-term impacts on yields when it is highly certain that large changes in management practices, cultivars or other technologies will take place in the decades to come. To use the models in such a fashion is clearly unwise, and the results of past crop impact analyses must therefore be interpreted critically. Crop-climate models can possibly be more useful as tools for facilitating short-term management and response, rather than for predicting long-term impacts of climatic change. Some potential uses include: analyses of the sensitivity of yields to climate variables; analyses of the interaction of CO₂ and climate variables on yields; determination of the effects of alternative management practices on yields under different climatic conditions; or assessments of crop potentials in given regions.

Further application of crop-climate models for assessing the problems of climatic change should proceed cautiously and should be based on careful model evaluation. The kinds of applications envisaged in climate impact assessment may extend well beyond the original purpose of many models. Careful validations of existing crop-climate models are required to demonstrate their capability for accurately testing the sensitivity of crops to climatic change. Of the 49 models reported in the WMO survey noted above, only 70% were claimed to have been validated. In some cases, model validations that are more rigorous than those already performed by the modellers themselves may even be required (WMO, 1985).

9.4.2 MarginalSpatial Analysis

Casting a critical eye, the astute observer (e.g. Cooper, 1978) will comment that changes in climate surely will not be unnoticed by agriculturalists. Will not spatial shifts in cropping patterns occur to compensate for climatic changes? And will not such shifts in crop area modulate the impacts on yields and production? If so, it becomes important to identify and estimate the effects of climatic change at the margins of production.

As noted by Parry and Carter (1984), the concept of 'marginality' can be construed in a number of waysspatial (or environmental), economic and social. There has been a particular research interest in the environmentally marginal areas where the effects of climatic change may be felt acutely. One environmental case is where there exists an apparent mismatch of environment and agriculture (coffee growing in the cooler, thermally marginal regions of southern Brazil). In this situation, a slight change in climate could have large, areally widespread effects on `maladapted' agriculture. Steep climatic gradients (temperature gradients along highland slopes in Peru or precipitation gradients across the Sahel) pose another type of environmental marginality in which climatic change could substantively alter the growing environment within short distances and overwhelm even previously well-adapted agricultural activities. Finally, marginality can be viewed as a spatial zone of transition between alternative agricultural or other land uses (as between wheat growing and livestock grazing in the semi-arid US Great Plains) as a function of comparative economic advantage vis-a-vis the natural environment.

Marginalspatial analysis is concerned both with the agricultural impacts in these zones, and with the spatial shifts in the boundaries of, say, crop types, profitability or economic risks that might take place as a consequence of climatic change. Schematically, this is depicted simply in Figure 9.9. Two specific approaches to the problem can be discerned.

Figure 9.9 Marginalspatial analysis. This approach is concerned with the effects of climatic change on yields at the margins of production, and with the spatial shifts in crop area or other characteristics of agriculture that might result

The SpatialEcologic Approach

One approach rests heavily on the `mis-match' connotation of marginality and implicitly draws a strong parallel between natural ecosystems and agriculture. The underlying premise is that the spatial configurations of crop regions are determined largely by the natural environment. It assumes that particular crop types or agricultural systems adapt to climate in the same way that the spatial patterns of tropical rainforests, savannas or mixed temperate forests are influenced by global climate regimes. Any long-term change in climate creates a 'mis-match'-a disequilibriumwhich will prompt agriculture to re-adapt. As in natural ecosystems where the spatial manifestation of ecosystem response is most pronounced at the ecotone, agriculture will be most sensitive to climatic change at the margins of the crop region and will expand or contract as average environmental conditions change. Hence the term 'spatialecologic' for the approach. This characterization is based on only a handful of studies, most of which are concerned with North American grain production.

For example, in two separate studies, by Newman (1980; 1982) and by Blasing and Solomon (1983), the possible shifts in the U.S. Corn Belt as a result of climatic change were estimated. Notwithstanding some differences in averaging periods, climate data and variable definition, these two studies were essentially alike in approach. The approach required: (1) The identification and quantification of the environmental variables (or indices thereof) that limit the spatial extent of the crop region; (2) The selection of scenarios of climatic change and the modification of meteorological records accordingly; and (3) The calculation of effects on key climatic constraints (e.g. growing season length) and the consequent spatial shift in crop region. Both studies proceeded on the assumption that the Corn Belt is limited in its northern extent by the length of the frost-free growing season and by the thermal requirements for maturation, and in its western extent by inadequate soil moisture. Accumulated growing degree-days (GDDs)a timetemperature index usually expressed as the number of degrees over a base level for growth (10 °C in this case) per day summed over the growing seasonaccount for these factors in a single index. For this reason, and because the existing northern boundary of the Corn Belt parallels the GDD isolines rather closely, both studies used changes in GDDs from various scenarios of climatic change to approximate the possible geographical shifts in the crop region.

The results of both studies are presented in Figures 9.10 a and b. Newman (1980; 1982) found that a climatic warming would displace the Corn Belt 175 kilometres per degree C in a north-by-northeast direction. Blasing and Solomon (1983) obtained the same results, although the magnitude of the displacement was not quite as large. The latter study also examined the added effects of higher precipitation (indicated by GCM scenarios) and the adoption of earlier planting dates; it was found that, to some degree, both counteract the spatial displacement resulting from higher average temperatures.

Similar spatialecologic studies for Canada suggest a potential northerly expansion of small grain production as a result of a lengthening of the growing season that would accompany a climatic warming, barring spatial limitations posed by poor soils or other environmental barriers (Williams, 1975; Williams and Oakes, 1978).

One troublesome aspect of these spatialecologic studies, in the absence of a sound physical explanation, is the connection that is made between an environmental index like GDDs and the boundaries of a crop region. A simple spatial correlation between the 1320 GDD isoline and the northern margin of the Corn Belt (as per Newman, 1980) does not by itself infer causal relationship, nor does it constitute firm ground for believing that a shift in the first will necessarily lead to an identical shift in the second. Indeed, taking an historical glimpse of the North American grain belts, one finds considerable movement of both the winter and spring wheat belts (Rosenberg, 1982) and the Corn Belt during the last one hundred yearswith and without appreciable changes in climate. Furthermore, it should be borne in mind that these studies do not consider crop 'yield' in any detailed fashion. Thus, we are given some approximation of possible spatial displacement of crop area, but little indication of how this might affect area-wide yields and productionwhich might, in turn, further influence crop production at the margins.

Figure 9.10 Estimations of the impacts of climatic change on the geographical extent of the US Corn Belt using the spatialecologic method. (a) Simulated shift based on growing degree days (GDD in ^°C) during the frost-free growing season (from Newman, 1980). (B) Shift for 3 °C temperature increase and 8 cm precipitation increase, distributed evenly over the year (Blasing and Solomon, 198.3). The solid black line indicates current location of the corn belt

In general, the major limitation of the spatial-ecologic approach is the implicit assumption (noted above) that managed ecosystems like agriculture will respond slowly to changes in climate in a manner analogous to natural ecosystems. This assumption ascribes a degree of 'climatic determinism' that seems unwarranted. In natural ecosystems, a mix of plant species compete with one another for available climate resourceslight, moistureover many seasonal cycles. Slow climatic changes may give the comparative advantage to some plants at the expense of others, and ecosystem boundaries may slowly change as a consequence. But for the vast bulk of the world's food crops, the choice of what seeds to sow, and where, is made afresh with each season. Cropping patterns are subject largely to human decisionmaking, rather than natural competition. While nature may set the ultimate geographical limits for crop growth, human beings still have plenty of room to manoeuvre. 

Another variation of marginal-spatial analysis partly recognizes these criticisms and addresses the problem of climatic change from the viewpoint of economic viability. The 'spatial-economic' approach is also concerned with the environmental margins. But unlike the previous approach, the spatialeconomic approach inclines toward the 'shift-in-risk' perspective: The focus is on the change in climatic risk and its economic impact on a season-to-season basis. This is assumed to be the mechanism behind long-term spatial shifts in cropping patterns. Implicit is the notion of risk evaluation and decision-making by farmers. In marginal areas, crop change or farm abandonment could be, and often has been, the choice.

Figure 9.11 Schema of the spatialeconomic approach as applied to high altitude cold margins (cf Parry. 1978). (a) The initial probability of harvest failure as a function of elevation (curve R. spatialeconomic risk) as derived from altitudinal differences in growing degree-days (curve C) and the probability of harvest failure due to insufficient warmth for crop maturation (curve D). P₁ to P₂ denotes those probabilities for which farming is a risky business, which can he described spatially as the 'marginal zone' (MN). (b) The effect of a climatic change (cooling. C to C₁) on spatialeconomic risk (an increase, R to R₁). The marginal zone shifts downward in elevation (M₁N₁) while the former marginal zone becomes uneconomic or 'submarginal'. With fluctuating climate, recurrent zones of marginal and submarginal land can be estimated and mapped (see Figure 9.12)

Figure 9.12 Recurrent marginality for oats cultivation in British Isles predicted for 1 ^°C decrease in mean temperature (from Parry, 1978)

The spatialeconomic approach is expressed clearly in the works of Parry (1981, 1985; Parry and Carter, 1984). Parry (1975, 1978) originally investigated the relationships between climate, agriculture and settlement at the margins of oat growing in the uplands of Scotland during the 18th and 19th century. There, the spatial margins of production occur at high elevations with steep temperature gradients. Parry surmised that, because of the steep gradients, climatic cooling (or warming) could substantially increase (or decrease) the year-to-year risks of harvest `failure' due to lack of sufficient warmth for crop maturation (as measured by GDDs). With slightly lower average temperatures, the risk of harvest failure in consecutive seasons, which is of critical importance to farmers, would be magnified (as discussed in Section 9.3.). If the risks become unacceptably high, the `marginal zones' (the elevations at which farming is assumed to be just barely profitable given the prevailing risks of harvest failure) would shift to lower elevations as farms are abandoned and settlements recede downslope. Lands that were formerly considered marginal would become 'submarginal' (too risky for farming), and formerly stable, economically viable farms would become marginal. Thus, with a fluctuating climate one would expect to find zones of recurrent submarginal and marginal cropland. Figure 9.11 depicts schematically the methodological procedure by which these spatial shifts are predictedor 'retrodicted' in this case (Parry, 1981). The results can then be mapped (Figure 9.12). Parry found support for his retrodictions in historical and archaeological evidence of upland settlement patterns.

Recent studies of upland oat-growing in Scotland have used an expanded climatic record to extend the analyses from the 17th century to the present (Parry and Carter, 1985). In this study, the coolest 50-year period (16611710) and the warmest 50-year period (1931-80) were compared with respect to estimated changes in the frequencies of harvest failures and the subsequent shifts in the marginal cropping zones (failure frequencies of between 1-in-10 and 1-in-50 years). This modest warming (0.7 °C) would lead to an upward shift in elevation of the cropping margins of about 85 metres (equivalent to about one million hectares of land if extrapolated to the whole of Britain).

Similar methods were applied by Z. Uchijima (1978) to examine the latitudinal shifts in GDD isopleths under anomalous weather conditions and the possible effects on rice production in Japan. Uchijima found that, in general, the spatial and temporal variability in GDDs increases at higher latitudes. For changes in temperature expected to occur with a return frequency of 1-in-30 years, the GDD isopleths would shift 150, 200 and 300 km northward or southward from their normal positions in southern, middle and northern parts of Japan, respectively. In the northern region of Hokkaido, thermally marginal for rice growing, a southward shift of this magnitude due to cooling could possibly result in decline in production of about 40%.

A recent, complementary study in Japan by T. Uchijima (1986) considers the possible altitudinal shifts in the limits of rice cultivation in the two northernmost, climatically sensitive districts of Hokkaido and Tohoku. With a GCM scenario of warmer temperatures under doubled atmospheric CO₂ (Hansen et al., 1983), the shifts were estimated to be large430 to 510 metres upslope.

Discussion

Two critical assumptions can be identified from the schematic diagrams in Figure 9.11. It is assumed first that the relationship between climate and yield (curve D, which can be equated to technology and management) remains constant over time. The pace of technological change in many parts of the world suggests that the effects of alternative assumptions should be examined. Second, it is assumed that the levels of acceptable and unacceptable risks (P₁ and P2) that define the zones of marginality and the spatial boundaries of crop production also remain constant over time. Again, in the light of changes in food demand, prices, farm security, government policy, etc., such definitions are bound to change in the future, especially in regions that are experiencing rapid social and economic development.

Whether realistic assessments of the global agricultural impacts of climatic change can be made depends on how the rather rigid assumptions of the marginalspatial analyses can be relaxed, and on how the methods can be adapted to other parts of the world, particularly the tropics and subtropics. Furthermore, there is a need to make better use of existing models, including crop-climate models, in order to specify with greater precision the connections between climate, climatic risks, yields and farming decisions. Finally, there needs to be systematic consideration of the adaptations and adjustments that could, or would, be made in the agricultural sector in the event of changing climate and higher CO₂.

9.4.3 Agricultural Sector Analysis

A group of studies that we label `agricultural sector analysis' has attempted to make improvements on all these fronts. These studies recognize explicitly that agriculture is one sector embedded in a larger economy, and that the impacts of climatic change will be felt at different levels (e.g. production, income, employment), both within the agricultural sector and between sectors (e.g. manufacturing, services). Estimation of the range of such regional impacts is important not only in its own right, but also because the parts of the sector(s) interact and have capacity for feedback and response. Two specific approaches are evident.

Integrated Regional Impact Analysis

One approach is to use a hierarchy of models, linking them in a sequential fashion to trace the `cascade' of impacts through the biophysical, economic and social components of a regional system. For example, a combination of crop impact analysis (using cropclimate models) and marginalspatial analysis allows one to answer two important questions: What is the magnitude of impact? And where does it occur? Outputs from this, in the form of altered average yields or yield probabilities, can be used as inputs to farm simulation models in order to estimate how yield changes might interact with management factors such as fertilizer use to cause changes in farm production and income. These results can then be used as inputs to regional economic inputoutput models to evaluate the downstream impacts elsewhere in the region (as on grain storage and transport, farm machinery and fertilizer manufacture, retailing or services). Thus there is an attempt to integrate the range of potential impacts and to test the sensitivity of complex regional systems to climatic change and perturbation (see Callaway et al., 1982, for a review of available models). This basic approach was followed, for example, by the U.S. Department of Transportation's Climate Impact Assessment Program (CLAP; Grobecker, 1974), one of the first large-scale climate impact assessments aimed at estimating the possible range of effects resulting from atmospheric ozone depletion (Glantz et al., 1982).

Recent work supported jointly by the International Institute of Applied Systems Analysis (IIASA) and the United Nations Environmental Programme (UNEP) has sought to refine the methods. The IIASA-UNEP Climate Impact Project has developed iterative procedures for testing the effects on agricultural systems of, say, changes in crop cultivars or managerial practices, thus adding a dynamic element that previous studies lacked. The project has pursued eleven case studies, both in high latitude and in semi-arid regions (the results are currently being prepared for publication. See Parry et al., 1986a,b.) The set of semi-arid studies concentrates on short-term climatic variability, particularly drought, with three scenarios comprising a 1-in-10 year event, a single extreme year, and a 'back-to-back' event (consecutive extremes). In the set of high latitude studies the emphasis is placed on medium and long-term climatic changes. Two scenarios are based on the instrumental record and are used to investigate the impacts of a recurrence of an anomalous decade of weather and an extreme weather year. A third scenario based on the gridded outputs from the GISS GCM (Hansen et al., 1983) is used to assess the possible impacts of a CO₂-induced climatic change.

To illustrate, the results of the high-latitude, long-term climatic change analyses are presented in Table 9.6. Some caveats are in order. First, the studies are very recent and not yet finalized, so the findings are unavoidably preliminary. Second, these results should not be regarded as predictions. The high level of uncertainty attached to GCM predictions of changes in regional climate is likely to be magnified as their impacts are traced through the regional agricultural and economic sectors. Rather, the studies should be regarded as experiments designed to evaluate the feasibility of an approach, namely the linking of GCM estimates of climatic change to models of impact.

The specific methods and assumptions vary considerably between the experiments. Most consider changes in both mean air temperature and precipitation, but in some cases precipitation changes are not deemed important (e.g. in Japan where rice is assumed to be fully irrigated). Several experiments assume technological change (e.g. winter rye cultivation in the Leningrad region), while others assume present-day technology (e.g. barley and oats cultivation in Finland). Most of the experiments treat the 2 x CO₂ climate as an equilibrium condition to be compared with the present baseline data, but several experiments introduce a time dimension by assuming a linear trend in climate between the present and future equilibrium conditions. The major assumptions are found in the footnotes to Table 9.6. These differences should dictate against direct comparison across the case studies.

Within each case study, however, the findings illustrate the hierarchy of impacts and linkages in the region. Consider the case of Saskatchewan, Canada. Under the climatic conditions simulated by the GISS model for 2 x CO₂, regionally-averaged mean air temperatures and precipitation for the growing season increased by 3.4 °C and 18%, respectively. Various indices were employed to estimate the possible equilibrium changes in agroclimate and biomass potential. Currently, low temperatures limit the duration of the frost-free season and act as a constraint to crop cultivation in the north of the Province. Under the scenario of climatic change, the effective temperatures for plant growth increased markedly-a 50% increase in the Effective Temperature Sum (relative to the baseline period, 1951-1980). Thornthwaite's Precipitation Effectiveness Index was used to assess the average moisture situation, which improved with higher precipitation and offset the effects of higher temperature (although the frequency of monthly droughts, as calculated from the Palmer Index, increased).

Table 9.6   Preliminary results from the IIASA/UNEP Climate Impacts Project  with particular reference to CO₂-induced climatic change in high latitude regions. These estimates describe only one set of scenario experiments. Others not presented here refer to different types of climatic change and to adjusted technology and management. A schema of the approach followed is given in the accompanying notes.' Unless otherwise stated, estimates are relative to the 1951-80 climate and to yields simulated for that climate, with management and technology at c. 1980 levels. Direct effects of CO₂ have not been considered (from Parry et al., 1986x) 

Study Area			Canada Saskatchewan	Iceland	Finland		Northern U.S.S.R.			Japan
					17South	18North	Leningrad	Cherdyn	Central Zone
Source for climate scenario			2GCM	2GCM	2GCM	2GCM	2GCM	2GCM	Arbitrary Change	2GCM
		Temperature	3 +3.4 ºC	10+3 9 °C	19+4.1 °C	19+5.0 °C	25+4.2 ºC	28+2.7 ºC	32+ 1.0 °C	41+3.5 °C
		Precipitation	3+18%	10+15%	20+73%	20+109%	25 +52%	28+50%	NIL	42-5%
		Effective temperature sums	4+50%		21+23%	21+37%
		Moisture index	5+ 1 to + 13%
		Drought frequency	6 x 3
		Cultivable area		11+482%
		Cropping limits								43+587m 44 +540m
		Biomass potential	7 +1 to +30%							45+9%
		Spring wheat	8 -25%		22 +13%			203% 30+16% 31+26%
		Winter wheat							33+28%
		Barley			232% 2432%	23+21% 24+ 12%			345%
		Oats							35-5%
		Winter rye					2613% 27+4%
		Rice								46+8% 477% 48+2% 49+9% 50+26% 51+5%
		Yield variability								32-27%
		Hay		12+64%					364%
		Pasture		13+48%
		Carrying capacity: pasture rangeland		14+242% 15+64%
		Carcass weight		16+9%
		Farm income	926%
		Farm employment	93%
		G.D.P.	912%
		Total employment	92%
		Additional costs							3722% 38+5% 39+5% 40+4%
		Food stocks								53+1.8MT/yr

    NOTES     

Data sources: Parry et al. (1986a). Names following entries refer to authors of relevant chapters.

1. Schema of approach followed by IIASA UNEP Climate Project:

2. Goddard Institute for Space Studies (GISS) General Circulation Model 2 x CO₂ experiment; output processed for impact experiments (Bach)

 CANADA (Saskatchewan)

3. May-August mean air temperature and precipitation (provincial mean) (Stewart).

4. Effective temperature sum above 5 °C base; approx. provincial average (William and Jones).

5. Precipitation effectiveness index (annual) (Williams and Jones).

6. Percentage months with Palmer Drought Index < - 4 (Williams and Jones); baseline = 3.1% (1950-82).

7. Climatic Index of agricultural potential (southern Saskatchewan) (Williams and Jones).

8. Provincial average simulated yields (Stewart), 

9. Income, employment and GDP relative to 1980 values (Fautley).

ICELAND

10. Single. representative station (Stykkishólmur)-mean annual air temperature and precipitation (Bergthorsson).

11. Taiga area (hypothetical) -(Bergthorsson). 12 National average hay yield (Bergthorsson). 13 Average experimental pasture yield (Björnsson and Helgadottir).

12. Sheep number on improved grassland. Management as for 1941-9 (Bergthorsson).

13. Sheep number with average carcass weight fixed at 1980-84 level (Bergthorsson).

14. carcass weight with number of sheep fixed at 1965-83 average level (Dyrmundsson and Jonmundsson).

FINLAND 

17. Helsinki area (Uusimaa province). 

18. Oulu province. 

19. Regional mean annual air temperature (Varjo). 

20. May-September precipitation (Varjo).

21. Effective temperature sum above 5 ^°C base; 1971-80 baseline (Varjo).

22. Simulated yields relative to 1959-83 baseline for adapted spring wheat variety with thermal requirements 120 GDD greater than for present variety; southern Finland (Rantanen).

23. Simulation using 1883-1983 baseline climate, relative to 1983 reference yield (Mukula).

24. Simulation using 1959-83 baseline climate, relative to 1983 reference yield (Mukula).

NORTHERN USSR

25. Mean annual air temperature and precipitation (lakimets and Pitovranov). 

26. Simulation relative to 1973-80 baseline (Iakimets and Pitovranov).

27. Simulation for 2035 given transient fertilizer trend and transient climate change for assumed doubling to CO₂ by 2050. Other technology fixed at 1980 levels (Iakimets and Pitovranov).

28. May-September mean air temperature and precipitation (Sirontenko). 

29. Simulated yield for present variety (Sirontenko).

32. Transient change from 1981 to 1995 (Kiselev).

37. Winter wheat

38. BarleyMinimized expenditure to maintain production

39. Oatsat 198(1 levels (Kiselev). 

40. Hay

JAPAN

41. July-August mean air temperature in Hokkaido district. T for Tohoku district = +3.2 ^°C (T. Uchijima).

42. June-September precipitation (all Japan) (Z. Uchijima and Seino). 

43. Altitudinal shift for rice (Hokkaido) (T. Uchijima).

44. Altitudinal shift for rice (Tohoku) (T. Uchijima).

45. Total net primary production (all Japan) (Z. Uchijima and Seino). 

46. Yield index (Hokkaido) (T. Uchijima).

47. Simulation for existing early-maturing variety in Hokkaido (Horie).

48. Simulation for existing early-maturing variety, but for average of annual computations; 1974-83 baseline (Hokkaido) (Horie).

49. As for note 48, but for new middle-maturing variety (Horie). 

50. As for note 48, but for new late-maturing variety (Horie). 

51. Yield index (Tohoku) (T. Uchijima).

52. Coefficient of variation for existing early-morning variety relative to 197483 baseline (Hokkaido) (Horoe).

53. Mean annual accumulation of national rice stocks (Tsujii).

The calculated effect on biomass potential was positive as well. Using the Climatic Index of Agricultural Potential to assess the combined effect of temperature and precipitation, increases in biomass potential ranged from 1% to 30% in southern Saskatchewan to 74% at Uranium City in the north.

These 'improvements' in agroclimate and biomass potential do not necessarily bode well for spring wheat production in the Province, however. The impact on Saskatchewan spring wheat, which covers much of the southern Province and contributes to about 1314% of total world wheat trade, was estimated for each crop district using a crop-growth simulation model, and averaged over three broad soil zones. The aggregate effect on total provincial production was a decrease of 25%. Total farm income was estimated to decrease by a similar proportion (relative to 1980 values), as determined from a farm simulation model. When these effects were used as input to regional input-output and employment models, the downstream effects of the climatic change were estimated in terms of change in farm employment (3%), total provincial employment in all sectors (2%) and total provincial Gross Domestic Product (12%). Of course, there are wide confidence intervals attached to all these estimates (and others presented in Table 9.6).

Although most of the estimates are based on the assumption of static agronomic and economic conditions, it is possible to evaluate various options that are available to offset or mitigate the impacts by altering some of these assumptions. In the case of Saskatchewan, for example, options that have been investigated include the substitution of winter wheat for spring wheat and the transfer of marginal crop land to pasture. Experiments such as these serve to generate a new set of impact estimates that can be compared with the initial estimates, and that can help in evaluating appropriate policies of response. This iterative procedure thus explicitly recognizes the potential ability of agricultural systems to respond to environmental change. Some of the estimates in Table 9.6 incorporate adjustments like new crop varieties or changes in fertilizer application.

Agricultural Systems Analysis

Agricultural systems contain many more such feedback mechanisms that, over time, can act to exaggerate or diminish the potential impacts of climatic change on crop yields or spatial cropping patterns. Farmers can change technological inputs, crop varieties or planting decisions, and governments can intervene through price support programmes, export subsidies or disaster payments. The market system itself helps to regulate through forces of supply and demand on price. How do these factors interact to affect agriculture in the face of changes in climate and climatic risk? That is the question addressed explicitly by agricultural systems analyses.

The diagram in Figure 9.13 is a simple illustration of some hypothetical feedbacks that could conceivably influence yields and production under conditions of changing climate. These feedbacks help explain why deteriorating (or improving) climate may not necessarily lead to a concomitant deterioration (or improvement) in yields and production.

Figure 9.13 Some feedbacks influencing crop yields and production over time. The agricultural systems approach to impact assessment examines the dynamics of agriculture and the mechanisms which can diminish or accentuate the primary yield effects of increased CO₂ and climatic change

The central role of price and government policy in the agricultural system is evident. Holding all other factors constant, a decrease in yields would depress production and food supply and could lead to an increase in price. Increased prices have the effect of increasing the amount of land actually harvested (in the US Great Plains, for example, about 8% of planted wheat land, usually the poorer marginal land, remains unharvested, on average, due to economic considerations), thereby raising commercial, area-wide yields (per planted area). Higher price (from any source, including exogenous government price supports) also has the effect of contributing beneficially to farm income and encouraging investments in labour and capital required to produce acceptable yields. The basic premise of agricultural systems analysis is that this economic and political context in which climate and agriculture interact is crucial to understanding the long-term effects of climatic change.

Within this context, one can focus on the problems of climatic change at various scales, from individual farm production to global agriculture and food trade. Callaway et al. (1982) describe three types of agricultural sector models that can potentially be used to assess the impacts of climatic change. Econometric models relate crop area, yield and production of various crops and regions to explanatory variables (sometimes including weather, but often not) through multivariate regression techniques using historical and cross-sectional data. Similar techniques are used to estimate national demands for export and domestic production. These empirical models attempt to describe the actual behaviour of the system. In contrast, normative (or mathematical programming) models, typically based on linear programming techniques, purport to demonstrate how farmers should behavethe 'optimal' behaviourto satisfy specified economic objectives. These models represent the workings of the production system in physical and technological terms, and specify the flow of resources at various stages of farm production. The farm unit can be representative of any level of regionalization and aggregated up to national scale.

At an international scale, agricultural trade modelsor, more generally, global modelslink up nations or world regions through the mechanisms of world trade in agricultural commodities. Such models can be either normative or econometric. They vary widely in specific method and dynamic qualities, from systems dynamic methods to rather static inputoutput formulations. A selection of major global models and their characteristics is shown in Table 9.7. The time horizons of these models vary from 10 to 200 years, and the geographical aggregation ranges from a single world unit to 106 individual countries. Although in several models yields are varied from year-to-year (based on past yield variability) to represent the influence of weather, none explicitly incorporates climate variables.

Table 9.7 Major global models and their characteristics (adapted from Robinson, 1985)

Model	Authors	Time horizon (yrs)	Method	Geographical aggregation	Aggregation of agricultural sector	Treatment of climate
SARUM	Roberts (1977)	90	dynamic	3 regions	4 agriculture	generally
	SARU (1978)		simulation		products. 1	omitted
			inputoutput		food product,
			econometric		3 agriculture
					inputs
MOIRA 1	Linnemann et al	45	algorithmic.
	(1979)		optimization,		1 commodity	repetition of
			econometric	106 nations		past yield
						variations
World	Mesarovic and	2550	dynamic	12 regions (basic)
Integrated	Pestel (1974)		simulation	17 regions	5 commodities
Model (WIM)	Hughes (1980)		inputoutput	(subregional)	3 land types	omitted
International	Hughes (1982)	25+	dynamic	10 regions	2 commodities,	represented by
Futures			simulation		5 land types	yield factor
Simulation
(IFS)
Latin America	Herrera	100	dynamic	5 regions;	livestock	omitted
World Model	Scholnik et al.		optimization	20 regions	crops
	(1976)			may exist
FUGI	Kaya and Onishi	1015	econometric,	14 to 62	4 sector (?)	omitted
	(various dates)		input-output
			(dynamic)
USDA	Rojko and	1020	econometric,	28 regions	up to 14	omitted
Grains, Oils	Schwarz (1976)		recursively		commodities
and Livestock			dynamic
(GOL)
Interactive	Enzer et al	20	cross impact	10 regions	grain as proxy	stochastic yield
agricultural	(1978)		interactive		for all foods	prediction
model			projection
IIASA/FAP	Parikh and	1520	linked national	23 countries	9 commodities	omitted
	Rabar (1981);		models general	and country
	Fischer and		equilibium	groups
	Frohberg (1982)		recursively
			dynamic

However, all the major global models can potentially be used to assess the impacts of rising CO₂ and climatic change by exogenously manipulating the yield components. The major obstacle to doing so is that the models have not been subjected to careful scrutiny with respect to their compatibility and reliability in this problem context (Robinson, 1985; Liverman, 1983). As pointed out by Robinson (1985), global models were not constructed with climate impact assessment in mind, and it is likely that model structure, geographical aggregation and temporal resolution may be inappropriate. (For recent reviews of global models, see OTA, 1982; Meadows et al., 1982; Hughes, 1981; Robinson, 1985; or Callaway et al., 1982.)

Very few explicit climatic 'experiments' have been conducted with global models, despite the recommendations to this effect made by the World Climate Conference over five years ago (WMO, 1979). None could be called 'definitive'. However, two studies, one carried out at NCAR (Liverman, 1983; NCAR, 1984) and the other for the National Defense University study (NDU, 1983), provide examples of the potential use (or misuse) of global models and some insight regarding the possible dynamic responses of the agricultural sector to climatic change.

The study by Liverman (1983) sought to evaluate a single global model, the International Futures Simulation model (IFS; Hughes, 1982) in terms of its reliability for climate impact assessments. The general structure of the IFS model is shown in Figure 9.14 along with the yield-specific relationships within the agricultural sub-component. Like its parent model, the World Integrated Model (Mesarovic and Pestel, 1974), the IFS model does not contain a specific climate component. Instead, climate is represented by a yield factor, a multiplier to yields that can be manipulated as a surrogate for weather. In conducting climate-related sensitivity analyses, Liverman (1983) varied the yield factor in order to examine the direction and magnitude of response of predicted yields, production, exports and imports, crop prices, reserve levels, global starvation and the like. These 'perturbed' runs were compared to `base' runs with no climatic change (yield factor = 1.0). Such analyses provide clues as to whether a model behaves sensibly.

The IFS model was perturbed with both gradual trend changes and single-year 'pulses'. In the slow trend analysis, the model's `climate' (the yield factor) was gradually altered beginning in 1985, reaching the maximum change (1.2 or 0.8, depending on the assumed direction of climate impact) in the year 2000. In the pulse analysis, a single year only (1985) receives the maximum change. The analyses were conducted at both regional (e.g. USA) and aggregate global levels (ten regions) and produced two interesting results with respect to yields and production.

Figure 9.14 The International Futures Simulation model: (A) the basic framework; (B) estimation of crop yield within the agricultural component (from Liverman, 1983, as adapted from Hughes, 1982)  

First, in the trend analysis the ±20% changes by 'climate' led to predicted yield and production changes in the year 2000 that were noticeably less than 20%. In the USA, for instance, predicted yields increased and decreased by about 1617% and 1415%, respectively (Figure 9.15a). This tells us that the model's internal adjustment mechanisms were able to dampen about one-quarter of the adverse yield impact and about one-fifth of the potential gain from climatic change. Similarly, at the global scale total crop production changed by 57% in both directions by the year 2000, as shown in Figure 9.15b. Thus, the agricultural system displayed a considerable capacity for absorbing the potential impact of a slow change in climateby about two-thirds. 

Figure 9.15 Simulated agricultural effects of perturbed `climate' versus control runs to the year 2000 using the International Futures Simulation Model (adapted from Liverman, 1983). (A) crop yields in the USA with slow trend change in yield factor to 20%; (B) world crop production with slow trend changes in yield factor to ±20%; effect on world crop production (C) and reserves (D) of a single 20% pulse in 1985

Second, single pulses in either direction created instabilities in subsequent years, with negative consequences. A sudden jump in yields of +20% flooded crop reserves and depressed prices, which caused less agricultural investment and a rather large drop in production and reserves in the next year. An oscillation was set in motion that took a number of years to disappear. With a 20% pulse, a similar oscillation occurred, but the magnitude of the effects was greater (Figure 9.15c,d). The average change in production over the 15-year period was minor compared to the base run, but the ups and downs generated by the pulse created greater total starvation (the model's indicator of societal impact at the global scale). If, indeed, the importance of climatic change lies in the frequency changes of disruptive climatic events (the 'shift-in-risk' view), this global model would have us believe that the disruptive events are the unusually 'favourable' as well as the unfavourable years for global crop yields.

The study by the National Defense University (NDU, 1983) sought to determine the global agricultural impacts of various scenarios of climatic change, from large cooling to large warming. Yield impacts estimated previously (NDU, 1980) were entered into the USDA GrainsOilseedsLivestock (GOL) model to simulate production, trade and other economic effects to the year 2000. The GOL model is an econometric equilibrium model of the agricultural sector with demand, supply and trade components for 28 world regions and 12 agricultural commodities. The model could be described as 'dynamically recursive' in that the estimated values of selected endogenous variables (e.g. price) in one time step are used as exogenous variables in subsequent steps (Callaway et al., 1982). For this reason alone, it would be expected that initial yield impact factors, used to force the model, would differ from the predicted yield and production levels produced by model simulations.

This expectation proved correct. The differences, however, were not consistent from region to region. The global model inflated initial yield impact values in some cases (e.g. USA grains) while deflating them in others (e.g. Australian grains). Not only did magnitudes vary, but the directions of change were even different in several cases (e.g. Argentina). Presumably, the predictions of the model reflect production re-adjustments and changing patterns of comparative trade advantage from changing prices, investment levels and shifts in land use as the model markets clear at each time-step to balance supply and demand. The region by region production estimates for a large warming scenario as per cent deviations from base level projections (no climatic change) are shown in Table 9.8.

Overall, for a large global warming the NDU study projects no change in net global grain production. However, some countries would gain appreciably (e.g. Canada, USSR) and others lose (e.g. USA, Australia, South Asia). The USSR becomes a net exporter, and the USA exports less and Canada more. The study concludes that the global impacts of climatic change on grain production are relatively small compared to the sensitivity of production to variations in assumptions about population growth rates, per capita income, agricultural investment and technological change.

Table 9.8 Simulated global grain production in the year 2000 under a large warming scenario, as a percent of base level projections¹ (adapted from NDU, 1983)  

Group/country		% from base level
I.	Developed Countries:	2.4
	United States	3.8
	Canada	6.0
	European Community	2.2
	Other Western
	Europe	2.0
	Australia	3.0
	South Africa	4.1
II	Centrally Planned Countries:	3.1
	Eastern Europe	1.1
	USSR	6.1
	China	0.7
III	Developing Countries	1.4
	Indonesia	0.5
	Thailand	3.6
	Other Southeast Asia	0.0
	India	1.8
	Other South Asia	2.0
	High Income North
	Africa/Middle East	2.8
	Low Income North
	Africa/Middle East	2.9
	Central America	2.8
	Brazil	0.3
	Argentina	2.6
IV	Total Above	0.0
V	Warming Countries'
	Total2	3.3
1 Large warming scenario (-T, P) = 1.4°C, 6% high-mid latitudes;
1.0°C, 2% mid-low latitudes; 0.75°C, 2% sub-tropics
2 Countries favourably impacted by warming (Canada,
E. Europe, USSR, China).

Discussion

One should be cautious about accepting these findings at face value, however. Serious criticisms have been expressed concerning the specific manner in which the NDU study was conducted (e.g. Stewart and Glantz, 1985). More generally, major questions can be raised concerning the reliability of global models for climate impact assessment. Few extensive validations of global models have been performed (Meadows et al., 1982). Global modellers face chronic problems of limited historical data (needed for calibration as well as validation). These are compounded by the occurrence of 'anomalous' years which hinder model validation: for example, sudden policy decisions (large jump in Soviet grain imports in the early 1970s or USA grain embargoes in the 1980s), exogenous sector changes (OPEC and oil prices in the mid-1970s) or unusual combinations of weather events (as in 1972). For these reasons an attempt to validate the IFS model by recalibrating on pre-1970 data and validating on post-1970 data gave poor results (Liverman, 1983). But this does not necessarily mean that the model itself is poor; it may only mean that there was too much `noise' to determine if it is valid.

This presents a dilemma: '...Is the real world so complex that no simplification (model) can capture its behaviour?' (Robinson, 1985). In the absence of solid validations, belief in the results of models rests largely on faith. Careful attempts at validation and sensitivity analyses are required. One slow, but potentially effective, solution to validation, given limited historical data, is to test model predictions against observed data for each new year. This is being pursued in an effort to validate further IIASA's Food and Agriculture Programme model (Frohberg, 1984).

If we set our sights lower than quantitative prediction of climate impacts, global models may serve us better. If one believes that a model behaves reasonably with regard to the direction and magnitude of change, then in a more qualitative fashion it could be used

• as a pedagogic tool for understanding the interactions of many variables; 

• to examine the effects of strategies and policies for responding to climatic change;

• as a framework for organizing what we know and do not know about the behaviour of the system.

In the context of CO₂ and climatic change, little has been made of global models for these purposes.

Even so, agricultural sector analyses tell us, in general, that crop impact analyses and marginalspatial analyses are only a start, not the answer. In the event of changing climate, the dynamics of the agricultural sector would, to some degree, readjust crop yields and production with the passage of time. It appears likely that in some regions of the world, yields and production may be just as, if not more, sensitive to changes in technology, price or policy as they are to changes in climate, even large ones. Since these factors are largely manipulatable, whereas climate is not, this should give us some confidence in the face of possible climatic change.

9.4.4 Historical Case Studies

An alternative approach to climate impact assessment asks, What impacts actually did occur during past climatic changes? Descriptive, historical case studies seek to shed light on possible future effects on agriculture by examining past situations in which nature provided the climatic `experiment'. The approach is complementary to the previous approaches in that it provides an empirical check on assumptions and model-derived estimates regarding crop yield impacts, spatial shifts in crop margins (other indicators of impact) or the long-term dynamic effects of adjustments and other feedback mechanisms of agricultural systems.

There exist scores of historical case studies that deal with agriculture and climatic change and variability (e.g. see list by Rabb, 1983). Several attempts have been made to pull together the threads of climate and history (e.g. Wigley et al., 1981; Rotberg and Rabb, 1980; Smith and Parry, 1981), but the implications for future changes in climate remain elusive. Even case studies dealing directly with specific slow climatic changes often find it difficult to generalize about climate's impact on agriculture and society. For example, both Le Roy Ladurie's (1972) search through European history for `times of feast, times of famine' and Post's (1977) investigations of the last great subsistence crises in Europe found the role of climate intermingled with social, political and economic factors and therefore difficult to define.

Others have pointed to the lack of demonstrable impact in particular historical cases as a sign of agriculture's resilience to climatic change. Wittwer (1980), for instance, notes that, in the USA, the state of Indiana experienced a total change of +2 °C over this century (with a 0.1 °C per,year trend from 1915 to 1945), while agriculture continued to grow and prosper. Similarly, Rosenberg (1982) claimed that, historically, the shifting spatial pattern of wheat varieties with differing climate tolerances in the US Great Plains is evidence of agriculture's potential adaptability to climatic change.

Most historical case studies (and other anecdotal examples), however, skim the peripheries of the CO₂ and climatic change issue and lack scientific rigour. Extraction and comparison of relevant conclusions from the literature are complicated by at least three problems. First, many studies focus on the distant past (e.g. the Little Ice Age) when agricultural technologies and socio-economic conditions were radically different from today. Second, many suffer from problems of time-coincidence (Parry, 1981); due to lack of control, the cause-and-effect relationship between climatic change and agricultural impact is not clearly established (e.g. the coincidence of drought and economic depression in the US Great Plains during the 1930s). And third, the literature represents an eclectic mix of different hypotheses (or lack thereof) and methods which renders individual studies largely incomparable. For these reasons, historical case studies often appear simply idiographic and non-generalizable.

There is much to be gained by designing studies of the impacts of past climatic changes to overcome these problems. One study currently underway, for instance, is systematically examining recent climatic `changes' (over a decade or so) in a number of climatic divisions in the USA, each with adjacent non-affected areas as control cases (Kates et al., 1984). By examining recent cases of actual changes in climate, it is possible not only to look for evidence of primary biophysical crop and yield effects, but also to determine if, or how, agriculturalists perceived the changes and adjusted to new conditions over time. That a large number of potential case studies exist at this scale in the USA has been demonstrated by Karl and Riebsame (1984) (see Figure 9.16 for example). At a much broader scale, a survey of 20th century climatic fluctuations in the Northern Hemisphere was recently reported by Wallen (1984).

It has also been suggested that studies of cases analogous to climatic change may prove fruitful: for instance, the slow depletion of irrigation water from the Ogallala Aquifer underlying the US Great Plains (Glantz and Ausubel, 1984), or cases of the migration of agriculturalists to regions of unfamiliar climate (Nix, 1985). Rosenberg (1982) suggests spatial `crop migration histories' as evidence of both climate impact and response at agricultural margins.

Furthermore, if changes in the frequencies of extreme events prove to be important manifestations of climatic change, then research on natural hazards could be particularly relevant (e.g. for global assessments see White, 1974; Burton et al., 1978). Because agricultural (and other) impacts of extreme events are disruptive and visible, the research emphases have been placed on analyses of individual events (e.g. Garcia, 1981, on the international impacts of droughts in 1972); on hazard perception and adjustment (e.g. Heathcote, 1973, and Saarinen, 1966, on Australian and US Great Plains droughts, respectively); on trends in vulnerability (e.g. Warrick, 1980, on US Great Plains droughts, or Kates, 1980, on global trends); or on theories of hazard vulnerability (see Hewitt, 1983, for a range of viewpoints). So far, however, there has been little attempt to link the methods and findings of this large body of research to problems of long-term climatic change (as per our discussion in Section 9.3, for instance). The opportunity to learn from our actual experience with climatic change and variability should not be overlooked.

Figure 9.16 Time series plots depicting climatic fluctuations of (a) decreasing temperature and (b) increasing precipitation, from 194359 to 196076 (from Karl and Riebsame, 1984)

9.5 SUMMARY AND CONCLUSIONS

In this chapter, we have reviewed the possible direct effects of CO₂ on crops, as derived primarily from glass-house experimentation, the various approaches and their applications and findings with regard to assessing the possible effects on agriculture from climatic change. From a global perspective, what can we conclude?

9.5.1 The Possible Impacts of Increased CO₂ and Climatic Change

One approach is to consider crop impact in a static fashion, that is, as if CO₂ doubling or climatic change were instantaneous with no reaction from agriculturalists or the agricultural system. From such `crop impact analyses' some clear differences exist between the effects of CO₂ and climatic change effects; between the core and margins of crop regions; and between impacts in higher and lower latitudes. With respect to direct CO₂ effects,

• A 'doubling' of ambient CO₂ concentrations has a positive effect on growth and yield of major food and fibre crops. These may range from 10% to 50% for C₃ plants to 0% to 10% for C₄ plants.

• Globally, the potential benefits of CO₂-enhanced yields might well be unevenly distributed because of the differences in where C₃ and C₄ crops are grown. For instance, tropical and sub-tropical regions of Africa and Latin America that are dependent on maize, sorghum or millets (C₄ plants) may be less favoured than rice, wheat, barley or potato (C₃ plants) regions in South Asia, North America or Europe.

• The positive growth and yield response from elevated CO₂ levels is obtained under most environmentally stressful as well as optimal conditions. Thus both the core and the margins of crop regions could benefit from increased CO₂ relative to current yield levels.

• In relative terms (i.e. per cent of control CO₂ levels), the growth and yield response is actually higher under some stressful environmental conditions, like moisture stress. This could possibly have the effect of decreasing interannual yield variability in some cases, as in drought-prone areas.

• In absolute terms, yield response to increased CO₂ concentrations is greatest under good growing conditions, including adequate soil nutrients.

In many developing countries where soil nutrients shortage is a chronic problem, the full benefits of enhanced yields may not be realized, particularly if phosphorus is deficient. 

While the rise in global atmospheric CO₂ concentrations will accumulate comparatively smoothly over time and uniformly over space, climatic changes are apt to vary in direction and magnitude from region to region, and to occur against a background of relatively large interannual climatic variability. As yet, the regional patterns of climatic change cannot be forecast reliably. This presents the major obstacle to predicting actual crop yield and production impacts from climatic change. However, the sensitivity of crop yields can be investigated by using scenarios of climatic changearbitrary, instrumental or GCM-derived. Employing crop-climate models, a number of studies have found that, in the absence of managerial adjustments or direct CO₂ effects,

• For the core areas of the North American and European mid-latitude grain regions, the probable effect of an instantaneous increase in average temperatures would be to decrease crop yields. For a 2 °C increase, grain yields might decline from 3% to 17%, as a rough approximation.

• The negative impact of higher temperatures on grain yield derives from associated increases in evapotranspiration, and from accelerated rates of plant development and a shortening of the period of yield formation.

• Increases in precipitation would tend to offset the reductions in grain yield from warming, while decreases in precipitation would accentuate them (even in more humid grain regions like Western Europe).

• The impacts of climatic warming at the margins of production could be less than, greater than (e.g. semi-arid margins) or in the opposite direction from (e.g. at the cold margins) those observed at the core areas of production, depending on local environmental conditions.

• Few systematic studies of the impacts of possible changes in climate have been conducted for the tropics and sub-tropics.

At all latitudes, the potential for severest adverse (or most beneficial) impacts of climatic change on crops may, in fact, be located in the marginal areas, variously defined. Localities with steep environmental gradients, with limiting temperature or precipitation amounts or with economic transition zones may be sensitive to even slight changes in long-term trends, particularly as they may result in shifts in the frequencies of extreme climatic events on a year-to-year basis. But this is to say nothing of the agricultural consequences of, or responses to, potential crop impacts. It may well be that because climatic variability is a feature of those marginal areas, agriculturalists have incorporated a large range of adjustments and adaptations to deal with year-to-year variations. Furthermore, the socio-economic consequences of yield impacts are important only at a local level, whereas yield effects of climatic change in core areas, where the largest proportion of food production takes place, could have major implications at national or international scales. Rarely, however, is the issue of `impact for whom' addressed in crop impact analyses.

The impacts of climatic change in marginal areas of agriculture might well be expected to elicit spatial shifts in crop areas or practicesthe concern of the 'marginalspatial approach' to impact assessment. Such shifts could be considered as either ecological adaptation to, or economic impacts of, climatic change. Research with an ecological orientation indicates that, at least in the mid- and high-latitudes, the potential shifts in crop boundaries based on average crop-climate associations alone could be on the order of hundreds of kilometres per °C change (e.g. rice in Japan, wheat in Canada or maize in the USA). Other marginalspatial studies interpret and predict possible spatial changes through effects on climatic risks and agricultural decision-making. Still, the potential for spatial re-adjustment looms large.

Spatial readjustment, of course, is only one way in which agriculture could respond to increasing CO₂ and climatic change. In Section 9.3 we characterized the range of responses as `bands' of adaptation and adjustment, and emphasized that the potential impacts of climatic change and variability are a function of the 'widths' of these bands at any given time. Much of the response capability is internal to agriculture: feedback mechanisms can help to self-regulate the system to environmental change over time. The issue is, how much?

This question is addressed by 'agricultural sector analyses'. One approach is to link models in a sequential fashion to identify, estimate and integrate the 'cascade' of impacts which may occur at the regional scale. At national and international scales, global models that focus on agricultural production, consumption and trade are one means of examining the interactions within the entire system, although they remain largely untested and unused for purposes of climatic impact assessment. Based on limited applications, it can be suggested that

• A large proportion of any potential adverse effects on yields and production as a result of gradual change in climate can be absorbed or avoided through policy and market feedback mechanisms.

• The disruption from single extreme years (which could become more or less frequent with global warming) could cause over-reaction in the system with oscillating impacts in subsequent years. Extremely favourable, as well as unfavourable, production years may cause similar effects.

• The impacts of climatic change on production in one region could be transferred to another over time through the network of global market and trade.

Rather than as findings, it is prudent to advance these points as hypotheses for further analyses.

The general lesson from agricultural sector analyses is that close attention needs to be paid to the dynamics of the system. Furthermore, the response of the system depends critically on the assumed rate of environmental change. Just as in the case of using GCMs to investigate the climate sensitivity to doubled CO₂, it becomes important to consider the transient response of agricultural systems as climate is changed over time.

In lieu of modelling approaches, `historical case studies' can draw upon actual experience with climatic change in order to investigate crop impacts and agricultural response. For various reasons, it has been difficult to generalize from existing historical case studies. However, there is ample opportunity to design studies which could provide valuable empirical evidence to balance and complement modelling results.

9.5.2 Further Considerations

As reflected in the structure and content of this review, assessments of the agricultural impacts of increasing atmospheric CO₂ have been neglectful in at least three general ways. First, specific studies of the effects of higher CO₂ concentrations and of climatic change have proceeded quite independently of one another, following different approaches at different scales of analysis. For the most part, the direct effects have been examined by laboratory experiments at the scale of single leaves or plants, starting at the enzymatic level. Studies of the effects of climatic change tend to rely on models of crop yields, production or agricultural activity at regional and global scales. The gulf between the two is wide and largely unsatisfactory. In reality, CO₂ and climate variables like temperature, precipitation and radiation will affect crops simultaneously and interactively, and should be studied accordingly across a range of scales.

Second, agricultural pests and diseases, which exact heavy tolls on crop yields worldwide and which account for a large portion of expended labour and capital in agricultural production, are also likely to be affected by climatic changes (and perhaps CO₂). Nevertheless, they are rarely included in any systematic fashion in CO₂-specific impact studies.

Third, the research emphasis has rested heavily on agricultural crops, particularly grains, in temperate regions. Yet for many parts of the world the possible effects of increased CO₂ and climatic change on grasslands and livestock production is a primary concern. Again, these effects have received scant attention in CO₂-specific impact studies.

In part, the above issues could be addressed by turning to existing literature in an array of relevant disciplines. But that would require a more exhaustive and discerning review than that attempted by this chapter. 

9.5.3 Some Next Steps

The overall conclusion is that we have barely scratched the surface with respect to assessing the possible agricultural impacts of increased CO₂ and climatic change. Problems abound. While hundreds of direct CO₂ experiments have been performed, these are in the safety of the scientist's glasshouse and not subject to the vagaries of natureweather, pests, diseasenor to the whims of human management practiced in the field. Extrapolations of results from climate-crop models, many of which have not been adequately validated, fail to consider the dynamic response capability of agriculture. Clear distinctions have not been made between the core and the margins of crop regions where entirely different patterns of yield impacts and agricultural response may be experienced. Modellers of global agricultural relations have rarely considered climate, one of the most basic determinants of crop production. We are stuck with patching together scattered studies; the global picture is far from complete. The research community could help in five ways.

First, systematic consideration of the combined effects of CO₂ and climate variables is needed. From the preceding review, it is clear that the effects are interactive, and that simply adding together independently conceived experiments and model results is neglectful of these interactions.

Second, crop-climate models are the principal tool for assessing the yield impacts of climatic change, but from a global perspective the reliability of available models for these purposes is not well established. An international collaborative effort of crop-climate model validations and cross-model comparisons would strengthen the methodological foundation for impact assessment.

Third, in all aspects we know least about the possible agricultural impacts in the tropics and sub-tropics and most about the temperate and high latitudes. The imbalance needs to be redressed.

Fourth, in practically all analyses, scenarios of climatic change are imposed on a crop or agricultural system for which 'impacts' are then estimated. Considerably less thought, a priori, has been given to how climate actually influences agricultural decision-making, management and, consequently, yields. Is it the shifts in climatic risks, the slow changes in climatic averages, or some combination thereof which matters most to agriculturalists? Given knowledge of the sensitivities and of the ability or inability of agriculture to cope with change, one can then ask how increases in CO₂ and climatic change could make a difference.

Finally, there needs to be a better integration of available methods in impact assessment. Crop-climate models, marginalspátial methods, and global agricultural models are rarely linked sensibly in order to build a coherent, realistic appraisal of agricultural impacts. Considerable testing and refinement of models is a necessary prelude to such an integrated approach, and the IIASA-UNEP project (discussed above) has taken some tentative, encouraging steps in this direction. This conclusion is consistent with the recent SCOPE review impact assessment and with the philosophy and recommendations of the World Climate Conference (WMO, 1979). In the long run, this is the goal which current climate impact activities should aim to contribute. 

NOTE ON AUTHORSHIP AND ACKNOWLEDGEMENTS

Of the two principal authors, R.A. Warrick assumed responsibility for organization of Chapter 9 and for preparation of sections dealing with the possible indirect effects of climatic change (9.1, 9.3, 9.4 and 9.5). R.M. Gifford wrote Section 9.2, the direct effects of higher CO₂ concentrations on crop plants. The collaborating author M. L. Parry, contributed to the portion of Section 9.4.3 concerned with integrated regional impact assessments, for which T. Carter prepared Table 9.6. In addition, D. Liverman provided guidance in the preparation of Section 9.4.3, dealing with agricultural systems analysis.

The authors wish to thank the following persons for commenting on earlier drafts: B. Bolin, W. Böhme, B. R. Döös, T. Carter, W. Degefu, H. W. Ellsaesser, H. L. Ferguson, F. K. Hare, P. G. Jarvis, J. Jäger, R. W. Kates, M. R. Kiangi, H. E. Landsberg, D. Liverman, W. J. Maunder, J. E. Newman, O. Preining, C. Sakamoto, B. R. Strain, P. E. Waggoner, M. M. Yoshino.

Many useful ideas and proposals were forthcoming from a small WMO/UNEP/ICSU Expert Meeting on the Reliability of Crop-Climate Models for Assessing the Impacts of Climatic Change and Variability, held in Geneva, 2125 May, 1984. The authors wish to acknowledge the contribution of the following invited experts at this meeting: J. Goudriaan, T. Hodges, J. L. Monteith, R. D. Stern, E. Ulanova, and T. M. L. Wigley.

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