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Potential wheat yields in Zambia—A simulation approach
Agricultural Systems 14 (1984) 171-192
Potential Wheat Yields in Zambia--A Simulation Approach H. van Keulen Centre for Agrobiological Research (CABO), Centre for World Food Studies (SOW), Bornsesteeg 65, 6708 PD Wageningen, The Netherlands
& W. A. J. de Milliano* National Irrigation Research Station, PO Box 187, Mazabuka, Zambia
SUMMARY A simulation model is presentedJbr the calculation oJ wheat yields as a function of radiation and temperature, other factors being considered non-constraining. Parametrization of the model is based on field experimentation in Zambia. The results of the model account for most of the observations obtained and it may be used therefore with reasonable confidence to predict potential wheat yields. It is shown that the model also performs wellfor other regions, with climatic conditions similar to those for which it is calibrated. The model is applied to evaluate the prospects.for wheat cultivation in various regions of Zambia.
INTRODUCTION In the last decade the Government of Zambia has adopted a policy of stimulating wheat production in order to become self-sufl~cient and to cut * Present address: CIMMYT, Londres 40-401, Apartado Postal 6-641, 06600 Mexico DF, Mexico. 171
Agricultural Systems 0308-521X/84/$03-00 © Elsevier Applied Science Publishers Ltd, England, 1984. Printed in Great Britain
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H. van Keulen, W. A. J. de Milliano
back on imports. Within this general framework, agronomic research is being carried out in combination with programmes on wheat breeding and crop management, to establish the agronomic potential of wheat yields, to study the impact of pests and diseases, to improve yield stability and to assess the environmental limits to wheat production. One of the problems in interpreting the results of long-term yield trials is the assessment of the relative influence of genotypic characteristics, environmental factors and management practices on the final result under a particular set of conditions. That difficulty limits the possibilities for extrapolation of results to other areas, where less detailed experiments have been carried out, or for generalization over longer time periods. The use of models, in which the influence of such factors is described quantitatively, may be helpful in analysing experimental results and applying them for the purpose of prediction. In this paper a model is proposed that describes the production of wheat as a function of environmental conditions. Since the data collected in one of the research projects in Zambia (de Milliano, 1983) were used to calibrate the model, it is descriptive rather than explanatory in some respects (Van Keulen, 1976).
DESCRIPTION OF THE M O D E L The model* calculates the dry matter production of a wheat crop during development from emergence to maturity, as a function of average radiation intensity and average air temperature. The dry matter produced is partitioned into roots, leaves, stems and grain as a function of ontogenetic development of the crop. Biotic and abiotic factors other than radiation and temperatures are assumed to be non-limiting. C O 2 assimilation
The basis for the calculation of dry matter production is the rate of gross CO 2 assimilation of the canopy. This rate is obtained from the CO 2 assimilation-light response curve of individual leaves of the species, the total green (leaf) area of the canopy, the spatial arrangement of the leaves * A completelisting of the program, written in CSMP, is available from the authors on request.
Potential wheat yields in Zambia--a simulation approach
173
and their optical properties. A method for calculating daily values of gross CO2 assimilation for arbitrary combinations of these variables, in relation to geographical latitude and incoming radiation intensity, was worked out by de Wit (1965) and amended by Goudriaan & van Laar (1978). The results of these calculations have been extensively validated under field conditions. In the model presented here, such values are introduced as tabulated functions with geographical latitude and Julian calendar day as independent variables, for both completely clear and completely overcast days. The functions used are those given by Goudriaan and van Laar (op. cit.) for a light-saturated rate of CO 2 assimilation of individual leaves of 30 kg h a - ' h - 1, a normal value for wheat leaves (Stoy, 1965; Dantuma, 1973; Thomas et al., 1979), although higher values have also been reported (Winzeler, 1979). The potential gross CO 2 assimilation on any particular day is obtained by first calculating the fraction of the day that the sky is overcast by comparing the measured level of global radiation with the maximum possible and next interpolating between the CO 2 assimilation Tables for fully clear and fully overcast days. When the canopy does not form a closed cover and not all light is intercepted, the rate of CO 2 assimilation is reduced in proportion to the fraction of the energy intercepted by the canopy. The latter is obtained from the leaf area index, assuming exponential extinction with an extinction coefficient of 0-6 (Goudriaan, 1977).
Respiration Part of the carbon fixed by the assimilation process is respired to provide energy for biological functioning of the organism. Two main components are considered in the present model. (a) Maintenance respiration, providing energy to maintain cells and their structure. Since accurate data on maintenance requirements of tissue are scarce, estimates have to be made on the basis of the chemical composition of the existing material (Penning de Vries, 1974). In the present model the maintenance requirement for leaf, stem and root tissue (expressed as the amount of glucose consumed per unit of dry weight per unit of time) is set at 0 . 0 1 5 k g k g - ' day -1 for the pre-anthesis phase and at 0.01 kg kg- 1 day - 1 for the post-anthesis phase, when the nitrogen content of the tissue is generally lower. The values apply at a
174
H. van Keulen, W. A. J. de Milliano
temperature of 25 °C; the effect of other temperatures is obtained by doubling the rate per 10°C increase in temperature. For the grains, a factor of 0.01kgkg -1 day -1 at 25°C is assumed throughout (Penning de Vries et al., 1983). (b) Growth respiration, associated with the conversion of primary photosynthates into structural plant material. The proportion of energy lost during this conversion depends on the composition of the material being formed (Penning de Vries, 1974). In the model an average 'production value'--that is, a weight efficiency conversion--of 0.7 kg of dry matter per kilogram of glucose is assumed for the vegetative material and 0.8 kg kg- 1 for the grains. Growth respiration is reflected in the difference between unity and the production value.
Crop phenology The development pattern, i.e. phenology, of a growing plant is characterized by the rate and order of appearance of vegetative and reproductive plant organs. The rate of development--that is, the inverse of the duration of a particular growth stage--is governed by genetic properties as well as by environmental conditions (Van Dobben, 1962). In the model, the phenological state of the crop is defined by a variable, DVS, the development stage, having the value 0 at emergence and 1 at anthesis (DC 70, Zadoks et al., 1974). Intermediate values are obtained as the ratio between the actual temperature sum, calculated as the integrated value of average daily air temperature above 0 °C after emergence, and the temperature sum required for anthesis. The latter variable is introduced as a function of the date of emergence of the crop, its value ranging from 820 degree-days (°Cd) (above a base temperature of 0°C) for a crop emerging in midsummer on 1 January to 950 °Cd for a crop emerging around 1 July. These values are based on experimental data. Clearly, very short duration genotypes have been used, since the average temperature sum for a large number of genotypes was found to be about 1100 °Cd (Van Keulen & Seligman, 1984). The differences in temperature requirement for anthesis in different seasons may be associated with photoperiodic influences (Angus et al., 1981), but the effects ofdaylength are difficult to describe quantitatively at this stage. The temperature sum required for maturity is also introduced as a function of emergence date (1720°Cd at 1 January, 1850°Cd at 1 July). The temperature sum for the
Potential wheat yields in Zambia---a simulation approach
175
post-anthesis phase is thus 900 °Cd, which appears to be constant for all genotypes (Van Keulen & Seligman, 1984). Partitioning of dry matter The calculated daily increment in total biomass is partitioned among various plant organs--roots, leaf blades, stems and sheaths and grain-with partitioning factors depending on the crop's development stage (Fig. l). fraction to organs 1.0 0.8.
ayes ~
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Fig. 1. The distribution of dry matter over various plant organs as a function of the development stage of the canopy.
In general, it is difficult to obtain reliable data for dry matter accumulation in below-ground organs, the more so since the processes of growth and decay proceed concurrently so that weights determined at any particular point in time may not be indicative of the amount of material invested in the roots. The function defining the fraction of material allocated to the roots is largely based on data supplied by Jonker (1958). The partitioning between leaf blades and other vegetative structures (leaf sheaths, true stem) is strongly schematized. It is assumed that until D V S is 0.45, only leaf blades are being formed, after which stem elongation starts and more of the assimilates are invested in these structures (see Rawson & Hofstra, 1969; Spiertz, 1977). Initially, it was assumed that after anthesis all available carbohydrates are used for grain
176
H. van Keulen, W. A. J. de Milliano
filling. It is also assumed that pre-anthesis assimilation does not contribute to grain yield, which certainly is an oversimplification (Stoy, 1965; De Vos, 1975; Vos, 198 I). It may therefore be worth while to study this point in more detail when subsequent improvements in the model are being considered. Leaf area growth
The increase in photosynthesizing area of the canopy follows directly from the growth rate of the leaf blades, by assuming a constant ratio of 20 m 2 of leaf per kilogram leaf dry matter (Aase, 1978). The green area of sheaths and stems is neglected during the pre-anthesis phase but that is, in general, a negligible fraction (Cackett & Wall, 1971; Fischer, 1983). Leaves have only a limited life-span and some of the earlier formed leaves will die before anthesis. In the model, senescence is taken into account only after anthesis associated with the sink-action of the developing grain.
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The relative death rate of the green area is defined as a function of average air temperature (Fig. 2). The values introduced here give reasonably realistic simulations of the decline in green area: sometimes physiological maturity is reached before all assimilating tissue has stopped functioning; in other cases the reverse happens. In this schematic description, the contribution of other green tissue to the assimilation process after anthesis is also taken into account.
Potential .'heat yields in Zambia---a simulation approach
177
Sink versus source
The model is essentially a source-oriented model in which dry matter accumulation is governed by assimilate production. In preliminary runs, however, it turned out that such a description could not account for the very low grain yields observed when crops matured during very warm periods. The accelerated maturation and leaf deterioration predicted by the model under such conditions were not sufficient to explain these low yields. Closer examination revealed that these very low yields coincided with high maximum air temperatures around anthesis. Two possible explanations could be postulated: the high rate of development associated with these high temperatures reduced the time period available for spikelet differentiation (Friend, 1965; Warrington et al., 1977) or the high temperatures p e r se inhibited fertilization or promoted abortion of grains, both resulting in reduced grain numbers (Hoshikawa, 1959). In both situations the sink-size, characterized by the number of grains present and a maximum possible growth rate per individual grain (Vos, 1981), could limit the rate of accumulation of dry matter in the grains. Since the actual grain numbers are not simulated, a function is introduced in the model, representing this 'sink'-effect: when maximum air temperature is above 25 °C, in the ten-day period containing anthesis, reduction f a c t o r f o r grain g r o w t h 1.0-
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178
H. van Keulen, W. A. J. de Milliano
only a fraction of the available carbohydrates is translocated to the grain, this fraction declining with increasing temperatures and reaching 0 at 35 °C (Fig. 3). The remainder is assumed to be stored in the vegetative tissue. Model specifications The model is written in CSMP (Continuous System Modelling Program) developed by IBM for its 360 and 370 series of machines. The method of integration used is RKSFX and the time step of integration is 10 days. Application of a smaller integration interval hardly affects the calculated dry matter production with this method of integration.
RESULTS OF THE MODEL Influence of different years In the first instance, long-term monthly radiation and air temperature data collected at the National Irrigation Research Station, NIRS (Meteorology Department, Lusaka, 1975) in Zambia, were used in the model (Fig. 4). average daily radiation i n t e n s i t y
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179
Potential wheat yields in Zambia---a simulation approach
As a test for the performance of the model, crops were assumed to be sown at two-weekly intervals starting from early December until early June. In the model, two-weekly emergence dates were applied starting from 15 December. In Fig. 5 the grain yields calculated for the various sowing dates are given, along with experimental data collected at the same station. The latter are averages, obtained over several seasons and from different genotypes. The trend with sowing date in the calculated grain yields closely resembles that of the measured values, although the former are lower. That may be due to the fact that, often, genotypes with longer growth duration were included in the experiments (see section below headed 'Sensitivity analysis'). grain yield t ha -e 7 NIRS,
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The low yields calculated for the crops sown in mid-summer (December/January) are the result of a short development period (80 days from emergence until maturity, of which only 40 days are taken up by the post-anthesis phase) due to the high temperatures. For these sowing dates maximum air temperatures during anthesis are around 28 °C, so that the sink-induced reduction factor for grain growth has a value of approximately 0.5 (Fig. 3). Concurrently, the high temperatures during
180
H. van Keulen, W. A. J. de Milliano
the grain-filling phase lead to rapid leaf senescence, so that effectivelyvery little is contributed to the grains during the last ten days before maturity. Crops emerging after the middle of March are in progressively more favourable conditions: the total growth cycle increases to I l0 days, the post-anthesis pliase increases to 60 days. Furthermore, maximum air temperatures during anthesis are below 25 °C for crops emerging after 1 April; hence, there is no reduction in grain growth rate due to a limiting sink size. Finally, a reasonable proportion of the assimilating area is maintained until the end of the growing period. The maximum calculated grain yield under these conditions, for the crop emerging on 1 May, is slightly over 5.8 t ha- 1. Crops emerging after that date are again subject to unfavourable environmental conditions, resulting in a sharp decline in calculated grain yields. These results indicate the existence of a fairly narrow optimum period for cultivation of irrigated wheat under the conditions prevailing at NIRS in Zambia. It may be concluded that the model provides a reasonably accurate quantitative description of the influence of radiation and temperature on wheat yields for the NIRS research station. Since the model is, to a large extent, descriptive, rather than explanatory, a further testing of the model under a wider range of environmental conditions is necessary. Such data are available, at least in part, for three experimental years at NIRS (1975-1977) (NIRS Annual Reports, 1975, 1976, 1977). The major environmental factor varying between years is air temperature, rather than radiation. In the model the average monthly values of maximum and minimum air temperature for each year were introduced, in combination with long-term average radiation data. The results, presented in Fig. 6, show substantial differences between the years. For 1975 the calculated grain yields are close to those of the 'average year', reaching a maximum of 6.0 t ha-1 for the crop emerging on 1 May. For the year 1976 the calculated grain yields are substantially higher than those for 1975, at all but the first two sowing dates, and a maximum level of 7-2 t ha- 1 is reached for the crop emerging at the end of April. The reason is that 1976 was much cooler throughout, resulting in longer total growth duration, about ten days extra, effective mostly in the grain filling period, hence a higher harvest index, smaller respiratory losses and a longer green area duration (Table 1). The year 1977 presents an opposite picture: especially during the second half of the year, when the crops emerging after 1 April mature, air temperatures were high, hence higher respiration losses, shorter leaf area duration, a lower value
Potential wheat yields in Zambia--a simulation approach
181
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15/12 3o,~2 3o/1 2~3 1~4 1~ 31/5 Fig. 6. Simulated grain yields for NIRS, Zambia, for wheat crops sown at different dates in three years, in comparison with measured yields. for the sink-size determined reduction factor and, consequently, a lower harvest index (Table 1). The available experimental data, given in Fig. 6 for comparison, confirm the general trend of the calculations. Some comments are necessary. At NIRS, actual wheat yields are very often reduced by disease incidence, such as Helminthosporium (Helminthosporium sativum), stem rust (Puccinia graminis) or leaf rust (P. recondita) or due to drought periods of up to 25 days. Consequently, the measured average yields are TABLE 1 Comparison of Some Yield Attributes for Three Years at NIRS, Zambia
Emergence date
2 March 1 April 30 April
Green area duration"
Harvest index
1975
1976
1977
1975
1976
1977
33 48 62
33 55 89
32 50 68
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0.41 0.48 0.52
0.31 0-32 0'40
" Leaf Area Index (LAI) times days after anthesis.
182
H. van Keulen, W. A. J. de Milliano
virtually always below the calculated values. For the two years with sowing dates during the rainy season (1976 and 1977), the yields of the highest yielding genotype correspond well with the calculated yields. In the irrigation season (1 April to 15 June) calculated yields in 1975 and 1977 correspond reasonably well with the observed yields, with the exception of one observation in 1975 which, for unknown reasons, falls well below the calculated yield. For 1976 the calculated yields are about 1.5tha -1 higher than the experimental yields. This result may, at least partly, be explained by heavy stem rust in the experiments. No chemical control of rust was carried out at NIRS, and even resistant genotypes were exposed to rust spores from neighbouring, heavily infected plots which may have resulted in yield loss (de Milliano, 1983). These results of the model show again that air temperature is the overriding factor controlling the wheat yield potential under the conditions prevailing at NIRS, when the supply of water and nutrients is maintained at near-optimum levels and the influence of pests and diseases can be minimized. Application to various locations in Zambia
In order to assess the possibilities for wheat in Zambia, the prospects for production in various regions of the country have to be surveyed. The model has run for a number of representative stations having widely different environmental conditions. The calculated yields, presented in the form of sowing date experiments, are given in Fig. 7, while some other results are summarized in Table 2. These data show that the assumptions underlying the present model result in distinctly different patterns of wheat production for the five stations. For sowings in the rainy season (sowing period, 15 December to 15 February) the calculated yields increase substantially from south (NIRS) to north (Mbala), coinciding with increasing altitudes, and decrease from central Zambia to the west, concurrent with decreasing altitudes. The highest experimental yields obtained at Mount Makulu for a crop sown in the rainy season were 3.0 t ha- 1 (Moono, 1979) and above (Raemaekers, 1981), which is higher than the calculated yield. The highest yield recorded at Mbala during the 1976/77 rainy season was 2.4tha - t (Salmon, 1977), which is well below the calculated level. This indicates that factors other than temperature are yield-constraining.
Potential wheat yields in Zambia--a simulation approach
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g r o i n yield t ha -1 7 1. Mt. M o k u l u 2, M p i k o 3. K o o m o 4. M o n g u 6 5.Mbolo 6.NIRS 5
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different dates. At the warmer sites, NIRS, Mongu and Kaoma, calculated yields for crops sown in the rainy season are relatively constant and do not exceed 2.0 t ha-1, indicating that wheat production under those conditions is expected to be marginal at best. At the higher altitude in Mbala, however, there is a small but marked increase in calculated yield when sowing is delayed from early January to mid-February. In practice, yields may increase with delayed sowings, as in a trial at Uningi Pans (Mbala) during the 1976/77 rainy season, sowings on 27 January, 7 February and 20 February giving mean yields of 1.1, 1.6 and 1.7 t h a - 1 respectively (Zam-Can, 1977). A decrease in yield with delayed sowing, however, also occurs as a result of Helminthosporium and drought (Zam-Can, 1980, 1981). For crops sown in the irrigation season, the calculated yields are consistently high (4.4 to 6.0 t h a - 1) from south (NIRS) to north (Mbala) (Table 2), as was also found experimentally (Moono, 1979). West of Lusaka, the calculated yield is not always high (e.g. at Mongu) and sowing time has a distinct influence (Kaoma and Mongu). In a yield trial at the latter location with irrigated wheat, emerging on 18 July, 1974, yields of short duration genotypes (0.2 to 1.8 t ha-1) correspond well with yields calculated for such genotypes.
15 ° 3 Y S 14 ° 4 8 ' S 15 ° 15'S
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Mount Makulu Kaoma Mongu
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TABLE 2
Altitude (m)
55' E 15'E 26'E 20'E
987 1 213 ! 402 1 673
I. 1 2.3 2.9 4.1
0.9 1.9 2.2 3.8
0.2 0.4 0.7 0.3
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1 213 I 152 1 053
2-3 !.2 1-0
1.9 1-1 0.8
0"4 0.1 0.2
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Maximum Minimum Difference
Maximum Minimum Difference
6'0 5.5 5'9 6-0
Irrigation season sowing period 15 April to 1 June
Rainy season sowing period 15 December to 15 February
Arrangement of locations: South to North and increasing altitude
Longitude
Calculated M a x i m u m a n d M i n i m u m G r a i n Yields (t h a - 1 )
185
Potential wheat yields in Zambia--a simulation approach
For the seepage areas, in the west of Zambia, wheat sown with a low fertilizer application between mid-April and May is reported to yield up to 2.0tha -1 (Mounter, 1961; de Milliano, 1983). The maximum yield calculated for a short duration cultivar is 4.0 t h a - 1, which possibly could have been achieved by the application of larger quantities of fertilizer. It may be concluded from these results that the performance of the model is satisfactory. Sensitivity analysis To establish the relative importance of some of the relationships and parameters applied in the present model, a partial sensitivity analysis was carried out. The predicted result of variations in the level of radiation is shown in Fig. 8. An increase of 20 ~ in total global radiation under otherwise identical conditions would result in an increase of only 10-20 ~ in grain yield. It appears, therefore, that the level of radiation is not the main factor causing the low yields of crops sown during the rainy season (December-February). A similar decrease in the overall radiation level would result in a yield depression of about 30 ~ since, in that case, lower rain yield he a
N I R S , a v e r a g e we(ather data
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186
H. van Keulen, W. ,4. J. de Milliano
total production leads to incomplete light interception during the postanthesis phase and hence to a more than proportional decline in grain production. The prominent influence of air temperature is illustrated in Fig. 9, where the predicted results are given for situations with an average temperature 20 % higher or lower than the measured long-term average. Higher temperatures result in very low yields, even under otherwise the most favourable conditions, the highest calculated yield being only about 1.0 t ha-~. The effect of lower temperatures is even more pronounced, especially in the rainy season, where calculated yields are more than tripled. Even for crops sown in the cooler irrigation season, the model predicts that lower temperatures would still lead to substantial increases in production, as could also be deduced from the results presented in Fig. 6. A question of practical importance that may be treated in this framework is the effect of genotypes having different growth durations, a trait that can be manipulated by breeding. Based on field experience (de Milliano, 1983), appropriate values for the temperature sum required for grain
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The influence of the length of the vegetative period on simulated grain yields for NIRS, Zambia, for wheat crops sown at different dates.
anthesis (1175-1350°Cd) and for full maturity (2075-2250°Cd) were introduced. The results for long-term average weather conditions at NIRS are presented in Fig. 10, showing that yield differences of about 0.75 t ha -1 are predicted between short duration genotypes and long duration genotypes for 'early' sowings up to mid-February. For crops emerging between then and mid-April the difference steadily increases TABLE 3 Comparison of Some Calculated Growth Characteristics for Short-Duration and LongDuration Genotypes, at NIRS, Zambia Emergence date
31 December 2 March 15 April 1 May
Total growth duration (days)
Green area duration ~
Harvest index
Short
Long
Short
Long
Short
Long
80 100 110 110
100 110 130 130
27 35 87 66
61 105 173 163
0.25 0.27 0.47 0.51
0.25 0.29 0.49 0.47
= LAI times days after anthesis.
188
H. van Keulen, W. A. J. de Milliano
with a maximum that is almost 2 t h a - 1 higher. For crops emerging after the beginning of May the calculated yields for long duration genotypes are lower because anthesis occurs later which results in lower values for the sink size-dependent reduction factor. Some of the calculated characteristics are shown in Table 3 for a number of representative emergence dates. These data indicate that the longer growth duration does express itself in a higher total dry matter production but not always in higher grain yields. The long duration genotype has, in general, a much longer green area duration due to the higher leaf area index at anthesis.
APPLICATION IN SOME OTHER SITUATIONS For further testing of the model, some published data were collected. The first data used are those from the optimally irrigated treatment of an experiment carried out in the central Negev desert of Israel (Hochman, 1982). Phenological development of the crop is introduced as a forcing function, characterized by the temperature sum required for anthesis ( = l l 5 0 ° C d ) and the temperature sum required for maturity ( = 1900°Cd). The simulated and measured crop growth curves are presented in Fig. 11, showing a very good fit. The calculated and measured grain yields are also in close agreement. Also, results presented by Doyle & Fischer (1979) on three sowing dates and two seed rates of wheat grown in Tamworth, Australia, in 1973 were used. Phenological development is again introduced as a forcing function (temperature sum for anthesis= l l50°Cd, temperature sum for maturity = 2000°Cd similar to the Negev values). The results are presented in Table 4. For the first sowing date the simulated and measured data are in close agreement, with some deviation towards the end of the growing period. However, the predictions become progressively less satisfactory with later sowing dates. The number of days to a total above-ground dry matter of 400 g m - 2 is reasonably well estimated for all three sowing dates, dry matter at anthesis only for the first and second, while dry matter at maturity is only reasonable for the first sowing. Doyle and Fischer state, however, that water shortage may have occurred towards the end of the season, which could be the reason for the observed discrepancies. The higher dry matter yields in the simulation are the result of a much longer green area duration than was observed in the
total a b o v e ground dry weight
S de P.oker '78/" 7g
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+---÷
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8
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/
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.
.
.
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Comparison of measured and simulated dry matter accumulation for a wheat crop grown in the central Negev, Israel.
TABLE4 SummaryofTamworth Data
Sowing day Emergence day Day of anthesis Day of maturity
1973/T1 °
1973/T2
1973/T3
Meas. Simul.
Meas. Simul.
Meas. Simul.
172 -279 328
-186 281 322
205 -291 339
-219 294 339
235 -310 351
-249 315 354
D4 Days from sowing to 400 g m - 2 Dry matter at anthesis (g m-2) Dry matter at maturity (g m-2)
83 940 1245
76 918 1415
72 630 1050
67 740 1455
64 590 845
58 753 1303
Dl Days from sowing to 400 g m - 2 Dry matter at anthesis (g m-2) Dry matter at maturity (g m-2)
95 580 1150
85 710 1140
82 460 860
73 704 1239
76 380 635
70 678 1117
= T1, T2, T3 refer to sowing time.
190
H. van Keulen, W. A. J. de Milliano
TABLE 5 Comparison of Measured and Simulated Results for Niamey, Niger
Grain yield (t ha- 1)
Measured
Simulated
2.4
0.0
__
1.1 o
° Calculated, disregarding influence of temperature on seed set.
experiments. The accelerated leaf senescence in the experiments could have resulted from water shortage. The results for an irrigation experiment in Niamey, Niger (Alio, 1980) are reported in Table 5. When simulated with the version of the model as applied in the previous examples, the crop fails to produce any grain, since the maximum air temperature during anthesis is between 35 and 40 °C. In reality, a grain yield of 2.4tha-~ was achieved, which is more than twice the amount (1.1 t ha-1) simulated even if the influence of air temperature on seed set is removed. Evidently, the model cannot predict yields for this environment. CONCLUSIONS The model described in this paper, which is a combination of explanatory and descriptive parts, performs adequately for different locations and sowing dates in Zambia and some other regions. It should be of value for the analysis of experimental results and for the prediction of yields in other parts of the tropics and subtropics, which should, however, not be too dissimilar in environmental conditions from the sites for which it has been tested. Especially, maximum air temperatures during the growing season should not exceed 30 °C. Such a model, which can establish the potentials for wheat production from a meteorological data model, can replace much costly and laborious experimentation. Further improvement of the model is necessary for wider application. REFERENCES Aase, J. K. (1978). Relationship between leaf area and dry matter in winter wheat. Agron. J., 70, 563-5. Alio, M. (1980). Facteurs limitants et rendement potentiel d'une culture du mil. M6moire du CILSS-PNUD-OMM, Centre r6gional de formation et d'application en agrom6t6orologie et hydrologic operationelle.
Potential wheat yields in Zambia--a simulation approach
191
Angus, J. F., Mackenzie, D. H., Morton, R. & Schafer, C. A. (1981). Phasic development in field crops, lI. Thermal and photoperiodic responses of spring wheat. Field Crops Res., 4, 269-83. Cackett, K. E. & Wall, P. C. (1971). The effect of altitude and season length on the growth and yield of wheat (Triticum aestivum L.) in Rhodesia. Rhod. J. agric. Res., 9, 107-20. Dantuma, G. (1973). Rates of photosynthesis in leaves of wheat and barley. Neth. J. agric. Sci., 21, 188-98. Dobben, W. H. van (1962). Influence of temperature and light conditions on drymatter distribution, development rate and yield in arable crops. Neth. J. agric. Sci., 10, 377-89. Doyle, A. D. & Fischer, R. A. (1979). Dry matter accumulation and water use relationships in wheat crops. Aust. J. agric. Res., 30, 815-29. Fischer, R. A. (1983). Wheat. In: Potential productivity of.field crops under different environments (Yoshida, S. (Ed.)). IRRI, Los Banos, Philippines. Friend, D. J. C. (1965). Ear length and spikelet number of wheat grown at different temperatures and light intensities. Can. J. Bot., 43, 345-53. Goudriaan, J. (1977). Crop micrometeorology: A simulation study. Simulation Monographs, Pudoc, Wageningen, 249 pp. Goudriaan, J. & van Laar, H. H. (1978). Calculation of daily totals of the gross CO 2 assimilation of leaf canopies. Neth. J. agric. Sci., 26, 373-82. Hochman, Z. (1982). Effect of water-stress with phasic development on yield of wheat grown in a semi-arid environment. Field Crops Res., 5, 55-67. Hoshikawa, K. (1959). Influence of temperature upon the fertilization of wheat grown in various levels of nitrogen. Proc. Crop Sci. Soc. Japan, 28, 291-5. Jonker, J. J. (1958). Root studies and subsoiling in the North-Eastern polder. PhD Thesis, Agric. Univ. Wageningen, Tjeenk Willink NV, Zwolle (Dutch with English summary). Keulen, H. van (1976). Evaluation of models. In: Critical evaluation of systems analysis in ecosystems research and management (Arnold, G. W. & de Wit, C.T. (Eds)). Simulation Monographs, Pudoc, Wageningen. Keulen, H. van & Seligman, N. G. (1984). Simulation of water use, nitrogen nutrition and growth of a spring wheat crop. Simulation Monographs, Pudoc, Wageningen. (In press.) Meteorology Department, Lusaka (1975). Climatological Summary for Zambia. Periods ending December, 1970, 66 pp. Milliano, W. A. J. de (1983). Improvement of wheat in Zambia using incomplete resistance against rusts. PhD Thesis, Dept. of Phytopathology, Agricultural University, Wageningen, 156 pp. Moono, D. M. S. (1979). Climate, phenology and yield of wheat in Zambia. Farming in Zambia, July 1979, 14-16. Mounter, B. E. (1961). Wheat in northern Rhodesia. Rhod. Agr. J., 58, 24-9. NIRS. Annual Reports for 1975, 1976 and 1977. Mazabuka, Zambia. Penning de Vries, F. W. T. (1974). Substrate utilization and respiration in relation to growth and maintenance in higher plants. Neth. J. agric. Sci., 22, 40 -4.
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Penning de Vries, F. W. T., van Laar, H. H. & Chardon, M. C. H. (1983). Bioenergetics of growth of seeds, fruits and storage organs. In: Potential productivity of field crops under different environments (Yoshida, S. (Ed.)). IRRI, Los Banos, Philippines, pp. 37-59. Raemaekers, R. N. (1981). Plant pathology report--rainfed wheat 1980-81. Mount Makulu Research Station, Plant Protection Section, Annual Report 1980-81, 26pp. Rawson, H. M. & Hofstra, G. (1969). Translocation and remobilization of t4C assimilated at different stages by each leaf of the wheat plant. Aust. J. biol. Sci., 22, 321-31. Salmon, D. F. (1977). Zam-Can Wheat Development Project. Intern. Rep. Spiertz, J. H. J. (1977). The influence of temperature and light intensity on grain growth in relation to the carbohydrate and nitrogen economy of the wheat plant. Neth. J. agric. Sci., 25, 182-97. Stoy, V. (1965). Photosynthesis, respiration and carbohydrate accumulation in spring wheat in relation to yield. Physiol. Plantarum Suppl. IV, 125 pp. Thomas, S. H., Thorne, G. N., Kendall, A. C. & Pearman, I. (1979). Stem and ear respiration and leaf photorespiration during grain filling: Their significance to yield. In: Crop physiology and cereal breeding (Spiertz, J. H.J. & Kramer, Th. (Eds)). Pudoc, Wageningen, pp. 96-101. Vos, N. M. de (1975). Field photosynthesis of winter wheat during the grainfilling phase under highly fertile conditions. In: Proc. Int. Winter Wheat Conf. Zagreb, Nebraska (Johnson, V.A. (Ed.)). Miscell. Publicn, 32, 251-5. Vos, J. (1979). Effect of temperature and nitrogen on carbon-exchange rates and on growth of wheat during kernel-filling. In: Crop physiology and cereal breeding (Spiertz, J. H.J. & Kramer, Th. (Eds)). Pudoc, Wageningen, pp. 80-9. Vos, J. (1981). Effects of temperature and nitrogen supply on post-floral growth of wheat; measurements and simulations. Agric. Res. Rep. 911, Pudoc, Wageningen, 164 pp. Warrington, I. J., Dunstone, R. L. & Green, L. M. (1977). Temperature effects at three development stages on the yield of the wheat ear. Aust. J. agric. Res., 28, 11-27. Winzeler, H. (1979). The carbon exchange rate of two spring wheat varieties in relation to photon-flux density and age. In: Crop physiology and cereal breeding (Spiertz, J. H.J. & Kramer, Th. (Eds)). Pudoc, Wageningen, pp. 75-9. Wit, C. T. de (1965). Photosynthesis of leaf canopies. Versl. landbouwk. Onderz. Agr. Res. Rep. 663, Pudoc, Wageningen, 57 pp. Zadoks, J. C., Chang, T. T. & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Eucarpia Bull., 7, 10 pp. Zam--Can. Zambia42anada Wheat Development Project. Annual Research Reports 1976-77, 1979-80 and 1980-81.
Potential Wheat Yields in Zambia--A Simulation Approach H. van Keulen Centre for Agrobiological Research (CABO), Centre for World Food Studies (SOW), Bornsesteeg 65, 6708 PD Wageningen, The Netherlands
& W. A. J. de Milliano* National Irrigation Research Station, PO Box 187, Mazabuka, Zambia
SUMMARY A simulation model is presentedJbr the calculation oJ wheat yields as a function of radiation and temperature, other factors being considered non-constraining. Parametrization of the model is based on field experimentation in Zambia. The results of the model account for most of the observations obtained and it may be used therefore with reasonable confidence to predict potential wheat yields. It is shown that the model also performs wellfor other regions, with climatic conditions similar to those for which it is calibrated. The model is applied to evaluate the prospects.for wheat cultivation in various regions of Zambia.
INTRODUCTION In the last decade the Government of Zambia has adopted a policy of stimulating wheat production in order to become self-sufl~cient and to cut * Present address: CIMMYT, Londres 40-401, Apartado Postal 6-641, 06600 Mexico DF, Mexico. 171
Agricultural Systems 0308-521X/84/$03-00 © Elsevier Applied Science Publishers Ltd, England, 1984. Printed in Great Britain
172
H. van Keulen, W. A. J. de Milliano
back on imports. Within this general framework, agronomic research is being carried out in combination with programmes on wheat breeding and crop management, to establish the agronomic potential of wheat yields, to study the impact of pests and diseases, to improve yield stability and to assess the environmental limits to wheat production. One of the problems in interpreting the results of long-term yield trials is the assessment of the relative influence of genotypic characteristics, environmental factors and management practices on the final result under a particular set of conditions. That difficulty limits the possibilities for extrapolation of results to other areas, where less detailed experiments have been carried out, or for generalization over longer time periods. The use of models, in which the influence of such factors is described quantitatively, may be helpful in analysing experimental results and applying them for the purpose of prediction. In this paper a model is proposed that describes the production of wheat as a function of environmental conditions. Since the data collected in one of the research projects in Zambia (de Milliano, 1983) were used to calibrate the model, it is descriptive rather than explanatory in some respects (Van Keulen, 1976).
DESCRIPTION OF THE M O D E L The model* calculates the dry matter production of a wheat crop during development from emergence to maturity, as a function of average radiation intensity and average air temperature. The dry matter produced is partitioned into roots, leaves, stems and grain as a function of ontogenetic development of the crop. Biotic and abiotic factors other than radiation and temperatures are assumed to be non-limiting. C O 2 assimilation
The basis for the calculation of dry matter production is the rate of gross CO 2 assimilation of the canopy. This rate is obtained from the CO 2 assimilation-light response curve of individual leaves of the species, the total green (leaf) area of the canopy, the spatial arrangement of the leaves * A completelisting of the program, written in CSMP, is available from the authors on request.
Potential wheat yields in Zambia--a simulation approach
173
and their optical properties. A method for calculating daily values of gross CO2 assimilation for arbitrary combinations of these variables, in relation to geographical latitude and incoming radiation intensity, was worked out by de Wit (1965) and amended by Goudriaan & van Laar (1978). The results of these calculations have been extensively validated under field conditions. In the model presented here, such values are introduced as tabulated functions with geographical latitude and Julian calendar day as independent variables, for both completely clear and completely overcast days. The functions used are those given by Goudriaan and van Laar (op. cit.) for a light-saturated rate of CO 2 assimilation of individual leaves of 30 kg h a - ' h - 1, a normal value for wheat leaves (Stoy, 1965; Dantuma, 1973; Thomas et al., 1979), although higher values have also been reported (Winzeler, 1979). The potential gross CO 2 assimilation on any particular day is obtained by first calculating the fraction of the day that the sky is overcast by comparing the measured level of global radiation with the maximum possible and next interpolating between the CO 2 assimilation Tables for fully clear and fully overcast days. When the canopy does not form a closed cover and not all light is intercepted, the rate of CO 2 assimilation is reduced in proportion to the fraction of the energy intercepted by the canopy. The latter is obtained from the leaf area index, assuming exponential extinction with an extinction coefficient of 0-6 (Goudriaan, 1977).
Respiration Part of the carbon fixed by the assimilation process is respired to provide energy for biological functioning of the organism. Two main components are considered in the present model. (a) Maintenance respiration, providing energy to maintain cells and their structure. Since accurate data on maintenance requirements of tissue are scarce, estimates have to be made on the basis of the chemical composition of the existing material (Penning de Vries, 1974). In the present model the maintenance requirement for leaf, stem and root tissue (expressed as the amount of glucose consumed per unit of dry weight per unit of time) is set at 0 . 0 1 5 k g k g - ' day -1 for the pre-anthesis phase and at 0.01 kg kg- 1 day - 1 for the post-anthesis phase, when the nitrogen content of the tissue is generally lower. The values apply at a
174
H. van Keulen, W. A. J. de Milliano
temperature of 25 °C; the effect of other temperatures is obtained by doubling the rate per 10°C increase in temperature. For the grains, a factor of 0.01kgkg -1 day -1 at 25°C is assumed throughout (Penning de Vries et al., 1983). (b) Growth respiration, associated with the conversion of primary photosynthates into structural plant material. The proportion of energy lost during this conversion depends on the composition of the material being formed (Penning de Vries, 1974). In the model an average 'production value'--that is, a weight efficiency conversion--of 0.7 kg of dry matter per kilogram of glucose is assumed for the vegetative material and 0.8 kg kg- 1 for the grains. Growth respiration is reflected in the difference between unity and the production value.
Crop phenology The development pattern, i.e. phenology, of a growing plant is characterized by the rate and order of appearance of vegetative and reproductive plant organs. The rate of development--that is, the inverse of the duration of a particular growth stage--is governed by genetic properties as well as by environmental conditions (Van Dobben, 1962). In the model, the phenological state of the crop is defined by a variable, DVS, the development stage, having the value 0 at emergence and 1 at anthesis (DC 70, Zadoks et al., 1974). Intermediate values are obtained as the ratio between the actual temperature sum, calculated as the integrated value of average daily air temperature above 0 °C after emergence, and the temperature sum required for anthesis. The latter variable is introduced as a function of the date of emergence of the crop, its value ranging from 820 degree-days (°Cd) (above a base temperature of 0°C) for a crop emerging in midsummer on 1 January to 950 °Cd for a crop emerging around 1 July. These values are based on experimental data. Clearly, very short duration genotypes have been used, since the average temperature sum for a large number of genotypes was found to be about 1100 °Cd (Van Keulen & Seligman, 1984). The differences in temperature requirement for anthesis in different seasons may be associated with photoperiodic influences (Angus et al., 1981), but the effects ofdaylength are difficult to describe quantitatively at this stage. The temperature sum required for maturity is also introduced as a function of emergence date (1720°Cd at 1 January, 1850°Cd at 1 July). The temperature sum for the
Potential wheat yields in Zambia---a simulation approach
175
post-anthesis phase is thus 900 °Cd, which appears to be constant for all genotypes (Van Keulen & Seligman, 1984). Partitioning of dry matter The calculated daily increment in total biomass is partitioned among various plant organs--roots, leaf blades, stems and sheaths and grain-with partitioning factors depending on the crop's development stage (Fig. l). fraction to organs 1.0 0.8.
ayes ~
06.
x
0.402-
stems
grains
x roots ~
~ 0'.5
developmentstage 10
Fig. 1. The distribution of dry matter over various plant organs as a function of the development stage of the canopy.
In general, it is difficult to obtain reliable data for dry matter accumulation in below-ground organs, the more so since the processes of growth and decay proceed concurrently so that weights determined at any particular point in time may not be indicative of the amount of material invested in the roots. The function defining the fraction of material allocated to the roots is largely based on data supplied by Jonker (1958). The partitioning between leaf blades and other vegetative structures (leaf sheaths, true stem) is strongly schematized. It is assumed that until D V S is 0.45, only leaf blades are being formed, after which stem elongation starts and more of the assimilates are invested in these structures (see Rawson & Hofstra, 1969; Spiertz, 1977). Initially, it was assumed that after anthesis all available carbohydrates are used for grain
176
H. van Keulen, W. A. J. de Milliano
filling. It is also assumed that pre-anthesis assimilation does not contribute to grain yield, which certainly is an oversimplification (Stoy, 1965; De Vos, 1975; Vos, 198 I). It may therefore be worth while to study this point in more detail when subsequent improvements in the model are being considered. Leaf area growth
The increase in photosynthesizing area of the canopy follows directly from the growth rate of the leaf blades, by assuming a constant ratio of 20 m 2 of leaf per kilogram leaf dry matter (Aase, 1978). The green area of sheaths and stems is neglected during the pre-anthesis phase but that is, in general, a negligible fraction (Cackett & Wall, 1971; Fischer, 1983). Leaves have only a limited life-span and some of the earlier formed leaves will die before anthesis. In the model, senescence is taken into account only after anthesis associated with the sink-action of the developing grain.
relative d e a t h rate of leaves .12 day -1
.04
,J I
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Fig. 2.
s
,/
.08
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The relationship between the relative death rate of leaf blades after anthesis and
average air temperature.
The relative death rate of the green area is defined as a function of average air temperature (Fig. 2). The values introduced here give reasonably realistic simulations of the decline in green area: sometimes physiological maturity is reached before all assimilating tissue has stopped functioning; in other cases the reverse happens. In this schematic description, the contribution of other green tissue to the assimilation process after anthesis is also taken into account.
Potential .'heat yields in Zambia---a simulation approach
177
Sink versus source
The model is essentially a source-oriented model in which dry matter accumulation is governed by assimilate production. In preliminary runs, however, it turned out that such a description could not account for the very low grain yields observed when crops matured during very warm periods. The accelerated maturation and leaf deterioration predicted by the model under such conditions were not sufficient to explain these low yields. Closer examination revealed that these very low yields coincided with high maximum air temperatures around anthesis. Two possible explanations could be postulated: the high rate of development associated with these high temperatures reduced the time period available for spikelet differentiation (Friend, 1965; Warrington et al., 1977) or the high temperatures p e r se inhibited fertilization or promoted abortion of grains, both resulting in reduced grain numbers (Hoshikawa, 1959). In both situations the sink-size, characterized by the number of grains present and a maximum possible growth rate per individual grain (Vos, 1981), could limit the rate of accumulation of dry matter in the grains. Since the actual grain numbers are not simulated, a function is introduced in the model, representing this 'sink'-effect: when maximum air temperature is above 25 °C, in the ten-day period containing anthesis, reduction f a c t o r f o r grain g r o w t h 1.0-
.8-
.6-
.2-
I lO
Fig. 3.
I
20
I
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The sink-induced reduction factor for grain growth as a function of maximum air temperature at anthesis.
178
H. van Keulen, W. A. J. de Milliano
only a fraction of the available carbohydrates is translocated to the grain, this fraction declining with increasing temperatures and reaching 0 at 35 °C (Fig. 3). The remainder is assumed to be stored in the vegetative tissue. Model specifications The model is written in CSMP (Continuous System Modelling Program) developed by IBM for its 360 and 370 series of machines. The method of integration used is RKSFX and the time step of integration is 10 days. Application of a smaller integration interval hardly affects the calculated dry matter production with this method of integration.
RESULTS OF THE MODEL Influence of different years In the first instance, long-term monthly radiation and air temperature data collected at the National Irrigation Research Station, NIRS (Meteorology Department, Lusaka, 1975) in Zambia, were used in the model (Fig. 4). average daily radiation i n t e n s i t y
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Long-term monthly average values of radiation, maximum and minimum air temperatures at NIRS, Zambia.
179
Potential wheat yields in Zambia---a simulation approach
As a test for the performance of the model, crops were assumed to be sown at two-weekly intervals starting from early December until early June. In the model, two-weekly emergence dates were applied starting from 15 December. In Fig. 5 the grain yields calculated for the various sowing dates are given, along with experimental data collected at the same station. The latter are averages, obtained over several seasons and from different genotypes. The trend with sowing date in the calculated grain yields closely resembles that of the measured values, although the former are lower. That may be due to the fact that, often, genotypes with longer growth duration were included in the experiments (see section below headed 'Sensitivity analysis'). grain yield t ha -e 7 NIRS,
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The low yields calculated for the crops sown in mid-summer (December/January) are the result of a short development period (80 days from emergence until maturity, of which only 40 days are taken up by the post-anthesis phase) due to the high temperatures. For these sowing dates maximum air temperatures during anthesis are around 28 °C, so that the sink-induced reduction factor for grain growth has a value of approximately 0.5 (Fig. 3). Concurrently, the high temperatures during
180
H. van Keulen, W. A. J. de Milliano
the grain-filling phase lead to rapid leaf senescence, so that effectivelyvery little is contributed to the grains during the last ten days before maturity. Crops emerging after the middle of March are in progressively more favourable conditions: the total growth cycle increases to I l0 days, the post-anthesis pliase increases to 60 days. Furthermore, maximum air temperatures during anthesis are below 25 °C for crops emerging after 1 April; hence, there is no reduction in grain growth rate due to a limiting sink size. Finally, a reasonable proportion of the assimilating area is maintained until the end of the growing period. The maximum calculated grain yield under these conditions, for the crop emerging on 1 May, is slightly over 5.8 t ha- 1. Crops emerging after that date are again subject to unfavourable environmental conditions, resulting in a sharp decline in calculated grain yields. These results indicate the existence of a fairly narrow optimum period for cultivation of irrigated wheat under the conditions prevailing at NIRS in Zambia. It may be concluded that the model provides a reasonably accurate quantitative description of the influence of radiation and temperature on wheat yields for the NIRS research station. Since the model is, to a large extent, descriptive, rather than explanatory, a further testing of the model under a wider range of environmental conditions is necessary. Such data are available, at least in part, for three experimental years at NIRS (1975-1977) (NIRS Annual Reports, 1975, 1976, 1977). The major environmental factor varying between years is air temperature, rather than radiation. In the model the average monthly values of maximum and minimum air temperature for each year were introduced, in combination with long-term average radiation data. The results, presented in Fig. 6, show substantial differences between the years. For 1975 the calculated grain yields are close to those of the 'average year', reaching a maximum of 6.0 t ha-1 for the crop emerging on 1 May. For the year 1976 the calculated grain yields are substantially higher than those for 1975, at all but the first two sowing dates, and a maximum level of 7-2 t ha- 1 is reached for the crop emerging at the end of April. The reason is that 1976 was much cooler throughout, resulting in longer total growth duration, about ten days extra, effective mostly in the grain filling period, hence a higher harvest index, smaller respiratory losses and a longer green area duration (Table 1). The year 1977 presents an opposite picture: especially during the second half of the year, when the crops emerging after 1 April mature, air temperatures were high, hence higher respiration losses, shorter leaf area duration, a lower value
Potential wheat yields in Zambia--a simulation approach
181
grain yield t h a -1 NIRSo
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15/12 3o,~2 3o/1 2~3 1~4 1~ 31/5 Fig. 6. Simulated grain yields for NIRS, Zambia, for wheat crops sown at different dates in three years, in comparison with measured yields. for the sink-size determined reduction factor and, consequently, a lower harvest index (Table 1). The available experimental data, given in Fig. 6 for comparison, confirm the general trend of the calculations. Some comments are necessary. At NIRS, actual wheat yields are very often reduced by disease incidence, such as Helminthosporium (Helminthosporium sativum), stem rust (Puccinia graminis) or leaf rust (P. recondita) or due to drought periods of up to 25 days. Consequently, the measured average yields are TABLE 1 Comparison of Some Yield Attributes for Three Years at NIRS, Zambia
Emergence date
2 March 1 April 30 April
Green area duration"
Harvest index
1975
1976
1977
1975
1976
1977
33 48 62
33 55 89
32 50 68
0.32 0-33 0.43
0.41 0.48 0.52
0.31 0-32 0'40
" Leaf Area Index (LAI) times days after anthesis.
182
H. van Keulen, W. A. J. de Milliano
virtually always below the calculated values. For the two years with sowing dates during the rainy season (1976 and 1977), the yields of the highest yielding genotype correspond well with the calculated yields. In the irrigation season (1 April to 15 June) calculated yields in 1975 and 1977 correspond reasonably well with the observed yields, with the exception of one observation in 1975 which, for unknown reasons, falls well below the calculated yield. For 1976 the calculated yields are about 1.5tha -1 higher than the experimental yields. This result may, at least partly, be explained by heavy stem rust in the experiments. No chemical control of rust was carried out at NIRS, and even resistant genotypes were exposed to rust spores from neighbouring, heavily infected plots which may have resulted in yield loss (de Milliano, 1983). These results of the model show again that air temperature is the overriding factor controlling the wheat yield potential under the conditions prevailing at NIRS, when the supply of water and nutrients is maintained at near-optimum levels and the influence of pests and diseases can be minimized. Application to various locations in Zambia
In order to assess the possibilities for wheat in Zambia, the prospects for production in various regions of the country have to be surveyed. The model has run for a number of representative stations having widely different environmental conditions. The calculated yields, presented in the form of sowing date experiments, are given in Fig. 7, while some other results are summarized in Table 2. These data show that the assumptions underlying the present model result in distinctly different patterns of wheat production for the five stations. For sowings in the rainy season (sowing period, 15 December to 15 February) the calculated yields increase substantially from south (NIRS) to north (Mbala), coinciding with increasing altitudes, and decrease from central Zambia to the west, concurrent with decreasing altitudes. The highest experimental yields obtained at Mount Makulu for a crop sown in the rainy season were 3.0 t ha- 1 (Moono, 1979) and above (Raemaekers, 1981), which is higher than the calculated yield. The highest yield recorded at Mbala during the 1976/77 rainy season was 2.4tha - t (Salmon, 1977), which is well below the calculated level. This indicates that factors other than temperature are yield-constraining.
Potential wheat yields in Zambia--a simulation approach
183
g r o i n yield t ha -1 7 1. Mt. M o k u l u 2, M p i k o 3. K o o m o 4. M o n g u 6 5.Mbolo 6.NIRS 5
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I
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I
31/5
Fig. 7. Simulated grain yields for six locations in Zambia, for wheat crops sown at
different dates. At the warmer sites, NIRS, Mongu and Kaoma, calculated yields for crops sown in the rainy season are relatively constant and do not exceed 2.0 t ha-1, indicating that wheat production under those conditions is expected to be marginal at best. At the higher altitude in Mbala, however, there is a small but marked increase in calculated yield when sowing is delayed from early January to mid-February. In practice, yields may increase with delayed sowings, as in a trial at Uningi Pans (Mbala) during the 1976/77 rainy season, sowings on 27 January, 7 February and 20 February giving mean yields of 1.1, 1.6 and 1.7 t h a - 1 respectively (Zam-Can, 1977). A decrease in yield with delayed sowing, however, also occurs as a result of Helminthosporium and drought (Zam-Can, 1980, 1981). For crops sown in the irrigation season, the calculated yields are consistently high (4.4 to 6.0 t h a - 1) from south (NIRS) to north (Mbala) (Table 2), as was also found experimentally (Moono, 1979). West of Lusaka, the calculated yield is not always high (e.g. at Mongu) and sowing time has a distinct influence (Kaoma and Mongu). In a yield trial at the latter location with irrigated wheat, emerging on 18 July, 1974, yields of short duration genotypes (0.2 to 1.8 t ha-1) correspond well with yields calculated for such genotypes.
15 ° 3 Y S 14 ° 4 8 ' S 15 ° 15'S
Mount Makulu Mpika Mbala
Mount Makulu Kaoma Mongu
Latitude
15 ° 4 6 ' S 15 ° 3 3 ' S 11° 5 4 ' S 8°51'S
NIRS
Location
TABLE 2
Altitude (m)
55' E 15'E 26'E 20'E
987 1 213 ! 402 1 673
I. 1 2.3 2.9 4.1
0.9 1.9 2.2 3.8
0.2 0.4 0.7 0.3
28 ° 15'E 24 ° 4 8 ' E 23 ° 0 9 ' E
1 213 I 152 1 053
2-3 !.2 1-0
1.9 1-1 0.8
0"4 0.1 0.2
Arrangement of locations: Central to West and decreasing altitude
27 ° 28 ° 31 ° 31 °
5"5 5"8 4.0
4"4 4.0 2"6
4"5 4"4 5'4 5"7
l.l 1"8 1.4
1-5 1.1 0"5 0.3
Maximum Minimum Difference
Maximum Minimum Difference
6'0 5.5 5'9 6-0
Irrigation season sowing period 15 April to 1 June
Rainy season sowing period 15 December to 15 February
Arrangement of locations: South to North and increasing altitude
Longitude
Calculated M a x i m u m a n d M i n i m u m G r a i n Yields (t h a - 1 )
185
Potential wheat yields in Zambia--a simulation approach
For the seepage areas, in the west of Zambia, wheat sown with a low fertilizer application between mid-April and May is reported to yield up to 2.0tha -1 (Mounter, 1961; de Milliano, 1983). The maximum yield calculated for a short duration cultivar is 4.0 t h a - 1, which possibly could have been achieved by the application of larger quantities of fertilizer. It may be concluded from these results that the performance of the model is satisfactory. Sensitivity analysis To establish the relative importance of some of the relationships and parameters applied in the present model, a partial sensitivity analysis was carried out. The predicted result of variations in the level of radiation is shown in Fig. 8. An increase of 20 ~ in total global radiation under otherwise identical conditions would result in an increase of only 10-20 ~ in grain yield. It appears, therefore, that the level of radiation is not the main factor causing the low yields of crops sown during the rainy season (December-February). A similar decrease in the overall radiation level would result in a yield depression of about 30 ~ since, in that case, lower rain yield he a
N I R S , a v e r a g e we(ather data
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186
H. van Keulen, W. ,4. J. de Milliano
total production leads to incomplete light interception during the postanthesis phase and hence to a more than proportional decline in grain production. The prominent influence of air temperature is illustrated in Fig. 9, where the predicted results are given for situations with an average temperature 20 % higher or lower than the measured long-term average. Higher temperatures result in very low yields, even under otherwise the most favourable conditions, the highest calculated yield being only about 1.0 t ha-~. The effect of lower temperatures is even more pronounced, especially in the rainy season, where calculated yields are more than tripled. Even for crops sown in the cooler irrigation season, the model predicts that lower temperatures would still lead to substantial increases in production, as could also be deduced from the results presented in Fig. 6. A question of practical importance that may be treated in this framework is the effect of genotypes having different growth durations, a trait that can be manipulated by breeding. Based on field experience (de Milliano, 1983), appropriate values for the temperature sum required for grain
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The influence of differences in air temperatures on simulatod grain yields for NIRS, Zambia, for wheat crops sown at different dates.
Potential wheat yields in Zambia--a simulation approach t h a "1 gram yield
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7
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187
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I 30/12
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The influence of the length of the vegetative period on simulated grain yields for NIRS, Zambia, for wheat crops sown at different dates.
anthesis (1175-1350°Cd) and for full maturity (2075-2250°Cd) were introduced. The results for long-term average weather conditions at NIRS are presented in Fig. 10, showing that yield differences of about 0.75 t ha -1 are predicted between short duration genotypes and long duration genotypes for 'early' sowings up to mid-February. For crops emerging between then and mid-April the difference steadily increases TABLE 3 Comparison of Some Calculated Growth Characteristics for Short-Duration and LongDuration Genotypes, at NIRS, Zambia Emergence date
31 December 2 March 15 April 1 May
Total growth duration (days)
Green area duration ~
Harvest index
Short
Long
Short
Long
Short
Long
80 100 110 110
100 110 130 130
27 35 87 66
61 105 173 163
0.25 0.27 0.47 0.51
0.25 0.29 0.49 0.47
= LAI times days after anthesis.
188
H. van Keulen, W. A. J. de Milliano
with a maximum that is almost 2 t h a - 1 higher. For crops emerging after the beginning of May the calculated yields for long duration genotypes are lower because anthesis occurs later which results in lower values for the sink size-dependent reduction factor. Some of the calculated characteristics are shown in Table 3 for a number of representative emergence dates. These data indicate that the longer growth duration does express itself in a higher total dry matter production but not always in higher grain yields. The long duration genotype has, in general, a much longer green area duration due to the higher leaf area index at anthesis.
APPLICATION IN SOME OTHER SITUATIONS For further testing of the model, some published data were collected. The first data used are those from the optimally irrigated treatment of an experiment carried out in the central Negev desert of Israel (Hochman, 1982). Phenological development of the crop is introduced as a forcing function, characterized by the temperature sum required for anthesis ( = l l 5 0 ° C d ) and the temperature sum required for maturity ( = 1900°Cd). The simulated and measured crop growth curves are presented in Fig. 11, showing a very good fit. The calculated and measured grain yields are also in close agreement. Also, results presented by Doyle & Fischer (1979) on three sowing dates and two seed rates of wheat grown in Tamworth, Australia, in 1973 were used. Phenological development is again introduced as a forcing function (temperature sum for anthesis= l l50°Cd, temperature sum for maturity = 2000°Cd similar to the Negev values). The results are presented in Table 4. For the first sowing date the simulated and measured data are in close agreement, with some deviation towards the end of the growing period. However, the predictions become progressively less satisfactory with later sowing dates. The number of days to a total above-ground dry matter of 400 g m - 2 is reasonably well estimated for all three sowing dates, dry matter at anthesis only for the first and second, while dry matter at maturity is only reasonable for the first sowing. Doyle and Fischer state, however, that water shortage may have occurred towards the end of the season, which could be the reason for the observed discrepancies. The higher dry matter yields in the simulation are the result of a much longer green area duration than was observed in the
total a b o v e ground dry weight
S de P.oker '78/" 7g
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Comparison of measured and simulated dry matter accumulation for a wheat crop grown in the central Negev, Israel.
TABLE4 SummaryofTamworth Data
Sowing day Emergence day Day of anthesis Day of maturity
1973/T1 °
1973/T2
1973/T3
Meas. Simul.
Meas. Simul.
Meas. Simul.
172 -279 328
-186 281 322
205 -291 339
-219 294 339
235 -310 351
-249 315 354
D4 Days from sowing to 400 g m - 2 Dry matter at anthesis (g m-2) Dry matter at maturity (g m-2)
83 940 1245
76 918 1415
72 630 1050
67 740 1455
64 590 845
58 753 1303
Dl Days from sowing to 400 g m - 2 Dry matter at anthesis (g m-2) Dry matter at maturity (g m-2)
95 580 1150
85 710 1140
82 460 860
73 704 1239
76 380 635
70 678 1117
= T1, T2, T3 refer to sowing time.
190
H. van Keulen, W. A. J. de Milliano
TABLE 5 Comparison of Measured and Simulated Results for Niamey, Niger
Grain yield (t ha- 1)
Measured
Simulated
2.4
0.0
__
1.1 o
° Calculated, disregarding influence of temperature on seed set.
experiments. The accelerated leaf senescence in the experiments could have resulted from water shortage. The results for an irrigation experiment in Niamey, Niger (Alio, 1980) are reported in Table 5. When simulated with the version of the model as applied in the previous examples, the crop fails to produce any grain, since the maximum air temperature during anthesis is between 35 and 40 °C. In reality, a grain yield of 2.4tha-~ was achieved, which is more than twice the amount (1.1 t ha-1) simulated even if the influence of air temperature on seed set is removed. Evidently, the model cannot predict yields for this environment. CONCLUSIONS The model described in this paper, which is a combination of explanatory and descriptive parts, performs adequately for different locations and sowing dates in Zambia and some other regions. It should be of value for the analysis of experimental results and for the prediction of yields in other parts of the tropics and subtropics, which should, however, not be too dissimilar in environmental conditions from the sites for which it has been tested. Especially, maximum air temperatures during the growing season should not exceed 30 °C. Such a model, which can establish the potentials for wheat production from a meteorological data model, can replace much costly and laborious experimentation. Further improvement of the model is necessary for wider application. REFERENCES Aase, J. K. (1978). Relationship between leaf area and dry matter in winter wheat. Agron. J., 70, 563-5. Alio, M. (1980). Facteurs limitants et rendement potentiel d'une culture du mil. M6moire du CILSS-PNUD-OMM, Centre r6gional de formation et d'application en agrom6t6orologie et hydrologic operationelle.
Potential wheat yields in Zambia--a simulation approach
191
Angus, J. F., Mackenzie, D. H., Morton, R. & Schafer, C. A. (1981). Phasic development in field crops, lI. Thermal and photoperiodic responses of spring wheat. Field Crops Res., 4, 269-83. Cackett, K. E. & Wall, P. C. (1971). The effect of altitude and season length on the growth and yield of wheat (Triticum aestivum L.) in Rhodesia. Rhod. J. agric. Res., 9, 107-20. Dantuma, G. (1973). Rates of photosynthesis in leaves of wheat and barley. Neth. J. agric. Sci., 21, 188-98. Dobben, W. H. van (1962). Influence of temperature and light conditions on drymatter distribution, development rate and yield in arable crops. Neth. J. agric. Sci., 10, 377-89. Doyle, A. D. & Fischer, R. A. (1979). Dry matter accumulation and water use relationships in wheat crops. Aust. J. agric. Res., 30, 815-29. Fischer, R. A. (1983). Wheat. In: Potential productivity of.field crops under different environments (Yoshida, S. (Ed.)). IRRI, Los Banos, Philippines. Friend, D. J. C. (1965). Ear length and spikelet number of wheat grown at different temperatures and light intensities. Can. J. Bot., 43, 345-53. Goudriaan, J. (1977). Crop micrometeorology: A simulation study. Simulation Monographs, Pudoc, Wageningen, 249 pp. Goudriaan, J. & van Laar, H. H. (1978). Calculation of daily totals of the gross CO 2 assimilation of leaf canopies. Neth. J. agric. Sci., 26, 373-82. Hochman, Z. (1982). Effect of water-stress with phasic development on yield of wheat grown in a semi-arid environment. Field Crops Res., 5, 55-67. Hoshikawa, K. (1959). Influence of temperature upon the fertilization of wheat grown in various levels of nitrogen. Proc. Crop Sci. Soc. Japan, 28, 291-5. Jonker, J. J. (1958). Root studies and subsoiling in the North-Eastern polder. PhD Thesis, Agric. Univ. Wageningen, Tjeenk Willink NV, Zwolle (Dutch with English summary). Keulen, H. van (1976). Evaluation of models. In: Critical evaluation of systems analysis in ecosystems research and management (Arnold, G. W. & de Wit, C.T. (Eds)). Simulation Monographs, Pudoc, Wageningen. Keulen, H. van & Seligman, N. G. (1984). Simulation of water use, nitrogen nutrition and growth of a spring wheat crop. Simulation Monographs, Pudoc, Wageningen. (In press.) Meteorology Department, Lusaka (1975). Climatological Summary for Zambia. Periods ending December, 1970, 66 pp. Milliano, W. A. J. de (1983). Improvement of wheat in Zambia using incomplete resistance against rusts. PhD Thesis, Dept. of Phytopathology, Agricultural University, Wageningen, 156 pp. Moono, D. M. S. (1979). Climate, phenology and yield of wheat in Zambia. Farming in Zambia, July 1979, 14-16. Mounter, B. E. (1961). Wheat in northern Rhodesia. Rhod. Agr. J., 58, 24-9. NIRS. Annual Reports for 1975, 1976 and 1977. Mazabuka, Zambia. Penning de Vries, F. W. T. (1974). Substrate utilization and respiration in relation to growth and maintenance in higher plants. Neth. J. agric. Sci., 22, 40 -4.
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H. van Keulen, W. A. J. de Milliano
Penning de Vries, F. W. T., van Laar, H. H. & Chardon, M. C. H. (1983). Bioenergetics of growth of seeds, fruits and storage organs. In: Potential productivity of field crops under different environments (Yoshida, S. (Ed.)). IRRI, Los Banos, Philippines, pp. 37-59. Raemaekers, R. N. (1981). Plant pathology report--rainfed wheat 1980-81. Mount Makulu Research Station, Plant Protection Section, Annual Report 1980-81, 26pp. Rawson, H. M. & Hofstra, G. (1969). Translocation and remobilization of t4C assimilated at different stages by each leaf of the wheat plant. Aust. J. biol. Sci., 22, 321-31. Salmon, D. F. (1977). Zam-Can Wheat Development Project. Intern. Rep. Spiertz, J. H. J. (1977). The influence of temperature and light intensity on grain growth in relation to the carbohydrate and nitrogen economy of the wheat plant. Neth. J. agric. Sci., 25, 182-97. Stoy, V. (1965). Photosynthesis, respiration and carbohydrate accumulation in spring wheat in relation to yield. Physiol. Plantarum Suppl. IV, 125 pp. Thomas, S. H., Thorne, G. N., Kendall, A. C. & Pearman, I. (1979). Stem and ear respiration and leaf photorespiration during grain filling: Their significance to yield. In: Crop physiology and cereal breeding (Spiertz, J. H.J. & Kramer, Th. (Eds)). Pudoc, Wageningen, pp. 96-101. Vos, N. M. de (1975). Field photosynthesis of winter wheat during the grainfilling phase under highly fertile conditions. In: Proc. Int. Winter Wheat Conf. Zagreb, Nebraska (Johnson, V.A. (Ed.)). Miscell. Publicn, 32, 251-5. Vos, J. (1979). Effect of temperature and nitrogen on carbon-exchange rates and on growth of wheat during kernel-filling. In: Crop physiology and cereal breeding (Spiertz, J. H.J. & Kramer, Th. (Eds)). Pudoc, Wageningen, pp. 80-9. Vos, J. (1981). Effects of temperature and nitrogen supply on post-floral growth of wheat; measurements and simulations. Agric. Res. Rep. 911, Pudoc, Wageningen, 164 pp. Warrington, I. J., Dunstone, R. L. & Green, L. M. (1977). Temperature effects at three development stages on the yield of the wheat ear. Aust. J. agric. Res., 28, 11-27. Winzeler, H. (1979). The carbon exchange rate of two spring wheat varieties in relation to photon-flux density and age. In: Crop physiology and cereal breeding (Spiertz, J. H.J. & Kramer, Th. (Eds)). Pudoc, Wageningen, pp. 75-9. Wit, C. T. de (1965). Photosynthesis of leaf canopies. Versl. landbouwk. Onderz. Agr. Res. Rep. 663, Pudoc, Wageningen, 57 pp. Zadoks, J. C., Chang, T. T. & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Eucarpia Bull., 7, 10 pp. Zam--Can. Zambia42anada Wheat Development Project. Annual Research Reports 1976-77, 1979-80 and 1980-81.