AB-DLO, Wageningen, Haren
DAN-UAW, Wageningen
Wageningen, 1995
1) AB-DLO, B.P. 14, 6700 AA Wageningen, les Pays-Bas
|
IER, Bamako AB-DLO, Wageningen, Haren DAN-UAW, Wageningen |
|
Rapports PSS Nº 2
Wageningen, 1995 |
Rapports du projet Production Soudano-Sahélienne (PSS)
« The research for this publication was financed by the Netherlands' Minister for Development Co-operation. Citation is encouraged. Short excerpts may be translated and/or reproduced without prior permission, on the condition that the source is indicated. For translation and/or reproduction in whole the Section DST/SO of the aforementioned Minister should be notified in advance (P.O. Box 20061, 2500 EB The Hague). Responsibility for the contents and for the opinions expressed rests solely with the authors; publication does not constitute an endorsement by the Netherlands' Minister for Development Co-operation » .
PGWA is a model for water and nitrogen limited growth conditions. It simulates phenological development and growth, storage and recirculation of non-structural carbohydrates, water use and forage exploitation of a perennial grass crop in tropical areas with a well-pronounced dry season, such as the Sahel and Sudan zones of West Africa. Development and parametrization of the model is based on Andropogon gayanus, a tall, tufted grass that forms part of the vegetation of many savannah areas throughout Africa south of the Sahara. Nutrient and organic matter balances were not considered in this version. A discussion on the main crop processes simulated is included.
Development of this simulation model was carried out at the Agrosystems department of AB-DLO within the project "Production Soudano-Sahelienne" (PSS).
I would like to express my gratitude to everyone of the Department of Agrosystems at AB, especially to Hein ten Berge, Herman van Keulen, Henk Breman and Sjaak Conijn for their contribution and support, and to Willem Stol and Henriette Drenth for their help.
I am very grateful to the interesting information provided by M. Traoré, R. Groot and A. Coulibaly from the Centre Regional de Recherche Agronomique de Niono (Mali) and A. Buldgen from the Unité de Zootechnie. Faculté des Sciences Agronomiques de Gembloux (Belgique). Equally I thank the ISRA Institute in Senegal, providing me with data on Andropogon gayanus.
Thanks, especially, to Peter Uithol for his excellent technical advices and his personal support.
Thanks also to the Instituto de Agricultura Sostenible of Cordoba (C.S.I.C.) in Spain which has financed my post-doctoral stay at AB-DLO.
Most cultivated grasses and a large number of valuable wild grasses belong to three subfamilies of the Gramineae (Bogdan, 1977): the festucoid (temperate grasses); the panicoid (tropical and subtropical grasses); and the chloridoid (a few cultivated tropical grasses and a number of valuable wild grasses of the tropics and of warmer areas of North America) subfamilies. The Andropogoneae is the largest tropical tribe in the panicoid subfamily.
The majority of tropical grasses belong to the C4 species. They have a higher optimum temperature (30-40 deg.C) and a higher optimum light intensity (50 000-60 000 lux) for photosynthesis than temperate grasses (C 4 species), and they also have a lower minimum plant nitrogen concentration (Penning de Vries & Van Keulen, 1991). Moreover, most of the tropical grasses are either short-day plants (flowering occurs earlier under short than under long photoperiods) or are photoperiod neutral.
Annual tropical grasses die at the onset of the dry season or even earlier, but in areas with less pronounced dry seasons they can survive longer than one season. All the tillers can potentially bear seeds and are uniform in structure. They constitute the main grazing in arid and semi-arid areas with insufficient moisture in the soil in the dry season to support perennial grasses. Annuals grasses can also be abundant in bush, light forest in less dry areas, overgrazed pastures and as fodder crops.
Perennial tropical grasses have normally a more complex structure than annual grasses. The morphology of grass plants varies especially in the geotropism of the shoots and the length of the internodes, which determine the spatial and hierarchical position of the perennating buds. The extremes are represented by (Humphreys, 1981):
- Tufted or tussock-forming grasses with erect culms having long internodes, that originate from basal shoots with short internodes (such as Andropogon gayanus). They usually form tufts that spread on the out side by repeated tillering or through rhizomes with very short internodes from which new groups of tillers originate. Tufted perennials usually develop two types of tillers: fertile or seed-producing tillers with elongated internodes and distant leaves; and sterile tillers with very short, practically invisible internodes and long crowded leaves often forming the bulk of the grazeable herbage. Sterile tillers can remain as such for years or elongate and flower in the next season. Tufted grasses usually form the main bulk in permanent, savannah-type grasslands and some are grown in leys and in sown permanent grasslands ( Panicum maximum, Pennisetum purpureum, Andropogon gayanus, Paspalum dilatatum, Hyparrhenia rufa, Melinis minutiflora and Cenchrus ciliaris);
- Creeping or sod-forming grasses with horizontal stems, either belowground (rhizomes) or close to the soil surface (stolons), from which erect or decumbent shoots originate with short internodes and form more even stands than tufted grasses. Rhizomatous grasses with creeping underground shoots usually occur in more moist areas or on sands, avoiding dry areas with hard and dry soil that is difficult to penetrate, common in swampy areas but not in seasonally waterlogged soil (e.g. Oryza longistaminata ). A number of rhizomatous grasses can form both rhizomes and stolons ( e.g. Cynodon dactylon, Pennisetum clandestinum). Purely stoloniferous grasses occur predominantly in the tropics and warm regions. The stolons creep horizontally on the soil surface rooting at the internodes ( e.g. Chloris gayana, Paspalum notatum). Stoloniferous, and sometimes rhizomatous grasses as well, are often pioneer species able to occupy quickly the bare ground of denuded pastoral land or arable land when it is left fallow. They can represent a certain phase in plant succession, often as a second stage after the dominance of annual weeds in fallows, and are in turn eventually replaced by tufted grasses or bush.
Perennial grasses have a fasciculate root system. Roots originating from the seed are known as primary roots, whereas secondary or adventitious roots develop from the nodes of tillers or creeping stems. Each tiller or group of tillers develops its own roots which makes it independent to a certain degree from other tillers or parts of the tuft with respect to obtaining water and mineral nutrients, although some connection between living tissues of different parts of the tuft can remain for a considerable time. Water and nutrients can be absorbed only by young root tips, densely covered with root hairs, old roots loose this ability. The majority of grass roots are found in the upper soil layers, but a number of roots can penetrate deep into the soil, down to 2 metres or deeper (Taerum, 1970).
Based on species composition and vegetation distribution,
wildlife and
livestock distribution and land use patterns, the Sahel and the
Sudan zones
were classified into (Le Houérou & Popov, 1981):
Sahel zone
- Saharo-Sahelian transition subzone (100-200 mm long-term
rainfall);
- Sahel zone proper subzone (200-400 mm);
- Sudano-Sahelian transition subzone (400-600 mm);
Sudan zone
- North Sudanian subzone (600-800 mm);
- South Sudanian subzone (800-1000 mm);
- Sudano-Guinean subzone.
Guinean zone
Perennial grasses are an important part of the herbaceous vegetation in the Sudan zone (Andropogon gayanus, Hyparrhenia dissoluta, Cymbopogon giganteus, Hyparrhenia rufa, Hyparrhenia smithiana, Andropogon tectorum, Andropogon ascinodis, etc.). They also used to play a significant role in the Saharo-Sahelian subzone and the Sudano-Sahelian subzone, but not in the Sahel proper subzone (Le Houérou, 1989). Saharan and Sahelo-Saharian species (Aristida pallida, Aristida pappossa, Cymbopogon schoenanthus, Panicum turgidum) are extremely drought-tolerant but sensitive to fire. Biomass of these species during the dry season is not enough fuel load and their tussocks are too sparsely distributed to carry fire over large distances. Perennial species of the Sudano-Sahelian subzone (Andropogon gayanus, Aristida longiflora, Hyparrhenia dissoluta and Cymbopogon giganteus), mostly belonging to the Andropogoneae tribe, are extremely tolerant to fire, but they are at the dry limit of their geographical distribution area. The paucity of perennial grasses in the Sahel zone proper has been explained by the combined effect of the fire, that limits the development of Saharian species, and water availability and crop exploitation, that limit the settlement of Sudanian Andropogoneae species.
At present, perennial grasses have almost disappeared in the Sahel zone, even in areas where there has been little or no grazing by livestock (Le Houérou, 1993). In the Sudan zone, Andropogon gayanus grows in areas with more than 700 mm of mean annual precipitation ( Breman & De Ridder, 1991).
During the last decades, the combination of an exponentially growing anthropozoic pressure (human and livestock populations) on the land, with prolonged drought periods has resulted in crop expansion over rangelands, a reduction of length of the fallow periods and severe degradation of savannahs in the Sahel zone (Le Houérou, 1989). This has lead to a sharp imbalance between natural resources and their exploitation, resulting in exhaustion and degradation of the soils. Degradation of soils has led to bare sand enclosed in a ring of species with a very short growth cycle on sandy soils and to bare plains with a hard crust, carrying only few microdunes with vegetation on loamy soils (Breman et al., 1991). Degradation of savannahs has led to elimination of perennial grasses (Andropogon gayanus, Aristida longiflora, etc.) and replacement of annual grasses of good fodder quality by less productive, palatable and nutritious annual species (Le Houérou, 1989). These processes of degradation, to a less extent, have also taken place in the north Sudan zone (Breman, per. comm.). Natural re-establishment of perennial grasses has not occurred after the drought, not even in areas without rangeland exploitation ( Breman & De Ridder, 1991).
Establishment of perennial grass crops, such as Andropogon gayanus, in integrated farming systems can be a feasible way of improving degraded rangelands or cropping areas (Dieng, 1991) and it could provide a fodder resource to reduce weight losses of livestock in the long dry season (Breman & De Ridder, 1991). Andropogon gayanus, a large tufted perennial grass, appears to be the best adapted to monsoonal climates with long dry seasons (Jones, 1979): it was common in protected Sudano-Sahelian areas prior to the drought of 1969-73 and it still occurs in the Louga and Dahra areas of Senegal (Le Houérou, 1993).
At present, more than 300 000 ha of poor acid soils in the tropical America have been planted with Andropogon gayanus cv. bisquamulatus. Seeds were introduced from northern Nigeria by CIAT in 1973. It has also been introduced as a temporary forage crop within a groundnut-cotton rotation in the groundnut basin of Senegal (Dieng et al., 1991). Technology for crop establishment and management was also developed.
This study was aimed to construct a model for a perennial grass
crop in the
Sahel and Sudan zones with the following future objectives:
- to provide an interactive tool for modellers and field
scientists to identify
those crop or soil processes for which more insight is required;
- to understand the added value of perennial grasses in the
production systems of the region compared to annual species;
- to identify agro-ecosystems in which the introduction or
stimulation of perennial grasses are technically and economically
feasible.
The model parametrization has been based on Andropogon gayanus data. Some parts of the model, such as storage and recirculation of reserve carbohydrates or crop survival, serve as research tools due to the early stage of understanding.
Andropogon gayanus Kunth belongs to the grass tribe Andropogoneae, subfamily Panicoideae. It is a tall, coarse, erect, perennial grass with culm height of 1-3 m and forms tussocks up to 1 m in diameter as results of short rhizome internodes and intravaginal branching.
The species occurs in Africa almost exclusively between the 400 mm and 1500 mm (Bowden, 1964) under a wide range of edaphic conditions: well adapted to low fertility conditions and acid soils (Amezquita et al., 1990). Andropogon gayanus is highly productive and moderately nutritious in pure stands without nitrogen fertilization (Jones, 1979) and a constituent of most of the savannahs of tropical Africa south of the Sahara (Bowden, 1964).
Four botanical varieties are distinguished (Keller-Grein &
Schultze-Kraft,
1990).
- The variety polycladus Hack. [syn. var. squamulatus
(Hochst.)
Stapf] is the most widely distributed of the four varieties: it
occurs north
and south of the equator in Africa.
- The variety bisquamulatus (Hochst.) Hack. has a
geographical
distribution almost identical to that of var. polycladus
north of the
equator, but it does not occur south of the equator. Bowden
(1963a) and
Mejía-M. (1984) suggested that bisquamulatus is
more vigorous and
aggressive than the other varieties.
- The variety gayanus (syn. var. genuinus Hack) has
a
distribution similar to that of var. bisquamulatus. It
occurs, often as
a dominating species, in seasonal swamps and flood plains
(Clayton, 1972;
Bogdan, 1977).
- The variety tridentatus Hack. mostly occurs in
semi-desert grassland
vegetation in West Africa (Bogdan, 1977).
The native habitat
of the
bisquamulatus and polycladus varieties is
characterized by a long
dry season of 2-9 month (Bowden, 1964). These varieties retain
green leaves for
much of this period, and rapidly start a regrowth at the onset of
the rains
(Bowden, 1963a; Bogdan, 1977). They are dominating species over
large areas of
the Guinean and Sudanian (Isoberlia-Hyparrhenia-Andropogon
) savannahs
and are also frequent in the drier Sahelian zone
(Acacia-Terminalia-Andropogon, Acacia-Combretum-sorghum
and
Combretum-Cenchrus savannahs) as well as in the West
African derived and
coastal shrub-savannahs (Keller-Grein & Schultze-Kraft,
1990).
The root system of Andropogon gayanus var.
bisquamulatus consists
of (Bowden, 1963b):
- short-branched rhizomes forming a compact mass near the soil
surface (90% of root dry matter);
- fibrous roots, fine, profusely branched, distributed just
beneath the surface and growing horizontally;
- vertical roots, fine, less branched and growing vertically;
- cord roots, short and thick that anchor the plant and store
starch.
Virtually all savannahs in which Andropogon gayanus occurs naturally are exposed to periodic burning, which removes almost all aerial parts. Andropogon gayanus is able to regrow after a fire because its rhizomes and roots are below the soil surface (Bowden, 1964).
The present version of the model, called PGWA, is based on the model PGWL-FSE, described in a provisional version of this report. It is written in FORTRAN-77 using the Fortran Simulation Environment FSE, version 2 (van Kraalingen, unpublished).
PGWA simulates regrowth and emergence, growth and development, senescence, water use, forage exploitation, storage and recirculation of reserve carbohydrates and survival of a perennial grass crop in tropical areas with a pronounced dry season, such as the Sahel and Sudan regions. Nutrient and organic matter balances are not considered in this version.
A listing of model variables is in the Appendix A.
Regrowth of perennial grasses starts at the beginning of the rainy season, which occurs some time after the first rains (Breman, 1991; de Bie, 1991; Cesar, 1992). In field experiments carried out in Senegal (Dieng, 1991) and in Mali (R. Groot, personal communication) the onset of growth of perennial grasses was observed after approximately 25 mm of rainfall in one or few subsequent days. Water availability is regarded the major factor determining crop regrowth and seed germination. Temperature is not considered a limiting factor under Sahel-Sudan conditions. Simulation of germination and regrowth is based on the emergence part of the spring wheat model of van Keulen & Seligman (1987)
Andropogon gayanus can be planted by using vegetative material in regions where labour availability and cost permit, but this system is not likely to be adopted because of the ease of seed production and harvesting (Spain & Couto, 1990). Crop establishment by sowing is assumed. Seeds of Andropogon gayanus have a very small caryopsis (900-1700 units per gram) and, hence, very limited nutrient reserves as a basis for initial development, which could explain the slow emergence observed under field conditions. Sowing depth of 1.5-2.5 cm (Bowden, 1963a) or 2-4 cm (Zimmer et al., 1983) is considered optimum. Germination is assumed to start and proceed through its various phases if soil moisture in the upper 10 cm of the soil exceeds a threshold value. This value is calculated as 1.3 (CRWCCG, file CROP.DAT) times the water content at wilting point. Emergence occurs after eight days of unhampered germination processes (Dieng, 1991). If the soil dries out below the critical soil water content within four days after the onset of germination, the process is halted but will resume after rewetting from the point where it stopped. However, if drying out occurs five or more days after the onset of germination and dry soil conditions persist for more than six days, seeds are assumed to die (sowing failure). Thus, drying out of the soil after the onset of germination will delay emergence and could also reduce the final number of seedlings and, therefore, initial crop biomass. To account for a poor emergence a reduction factor (RFIBWS, -) has been introduced, equal to the ratio between actual and maximum possible days with dry soil conditions after the onset of germination, to modify the initial crop biomass.
Crop regrowth is simulated similarly to crop emergence, but because of the higher availability of reserves in adult plants, sprouting occurs faster than seedling emergence. As in seedling emergence a reduction factor (RFIBWS, -), accounting for dry soil conditions during the regrowth process is used to modify initial shoot biomass.
Dates of regrowth and germination after sowing are calculated with a subroutine called PGCR (Appendix B).
Major processes like dry matter partitioning, storage and recirculation of assimilates and nutrients, senescence as well as nitrogen concentration and forage quality of perennial grasses depend directly or indirectly on the physiological age of the plant. Thus, a description of phenology in quantitative terms is required for crop modelling.
Most of the tropical perennial grasses are either short-day plants (flowering earlier under short than under long photoperiods) or are day-neutral. In the Sahel and Sudan regions, the main growth cycle of perennial grasses starts at the beginning of the regular rains (May to July) and ends in the cool dry season (November to February). Subsequently, if water is available in the soil, a new growth cycle can start after cutting or burning. During this new growing period, stem elongation and flowering can occur again when the cycle starts in the cool dry season (short day lengths), but not when the cycle starts later in the hot dry season due to the increasing day length (Dieng et al., 1991).
Andropogon gayanus is a short-day plant with a critical day length for flowering of 12-14 h (above which plants do not flower). Similar values of critical day length have been reported for other Andropogoneae grasses (Tompsett, 1976). Flowering and stem elongation of Andropogon gayanus occurred earlier when day length was shortened from 12 to 8 h. Moreover, there may be a juvenile phase of development of around 6 weeks when flowering and stem elongation cannot be induced, even under inductive photoperiods (Tompsett, 1976). Tompsett also observed a close association between stem elongation and flowering events in all his experiments, and suggested a close relation between the internal mechanisms controlling these events.
As the processes governing phenological development of
Andropogon
gayanus are not well understood, a descriptive model is used.
The life
cycle is divided into two phases using the main development
stages (DVS):
- pre-anthesis, from emergence or regrowth (DVS equal to 0) to
flowering (DVS equal to 1);
- post-anthesis, from flowering to grain maturity (DVS equal to
2);
Following grain maturity, all living aboveground biomass produced
during the
growing season will die. This phase is called shoot senescence
(SSS, -): from
grain maturity (SSS equal to 0) to complete death of the
aboveground biomass
(SSS equal to 1). During this phase DVS is equal to 2.
Crop phenology is simulated in a subroutine called PGPHE (Appendix B).
A significant correlation between latitude of the collection site (origin of the accession) and flowering date of Andropogon gayanus was found by Foster (1962) in Nigeria: accessions from northern Nigeria started to flower earlier than those from the southern part. Annual grass species from Mali behaved in the same way (de Ridder, 1979).
Foster (1962) throughout Nigeria and Dieng (1991) at Thies (Senegal) observed that flowering of Andropogon gayanus coincided with the end of the wet season. In Senegal, Monniaux (1978) found that time to flowering was closely correlated to the number of rainy days per year and that neither mean annual precipitation nor mean annual temperature improved the prediction. Flowering response of Andropogon gayanus accessions collected in Africa from sites between 8 and 12 deg.N was well synchronized with the end of the rainy season at similar or higher latitudes in tropical America, but at lower latitudes flowering started long before the end of the rainy season because of the shorter day lengths (Miles & Grof, 1990).
This flowering behaviour, that is a result of different photoperiod responses among genotypes, could be an adaptation of Andropogon gayanus to the length of the rainy season (Grof & Thomas, 1990).
Field data on the stage of stem elongation are very rough (Haggar, 1970; Dieng et al., 1991; Cesar, 1992). The close association between stem elongation and flowering of Andropogon gayanus observed by Tompsett (1976) appears not to be confirmed by these field data.
To simulate the rate of development during the pre-anthesis phase (DVR1, d-1) three options are offered by the current version of the model.
The last day of the rainy season (ER, in Day Of the Year, doy) was considered a good indicator of flowering date in the rainy season across the Sahel and Sudan regions (Foster, 1962; Monniaux, 1978; Dieng et al., 1991). In Mali (13deg.-17 deg.N), Hiernaux (1984) found a close relation between average date of the last rains and latitude (r = -0.90). The end of the rainy season is defined as the last day with rainfall of 15 mm or more, or the first day of the last five consecutive days with accumulated rainfall of 20 mm or more. For West Africa, the end of the rainy season could be estimated for any specific site from latitude (LATS, degrees) and longitude (LONS, degrees) as follows (Kowal & Kassam, 1978):
ER = 352 - 5.7*LATS - 0.7*LONS (1)
Table 1 presents experimental values of flowering date in the rainy season for Andropogon gayanus and estimation of end of the rains for several sites in West Africa.
Estimations of ER were close to the experimental flowering dates (Table 1), with the exception of Thies. Dieng et al. (1991) pointed out that flowering coincided with ER, but they did not define this environmental characteristic. Using the definition of Hiernaux (1984), ER occurred the julian calendar day 275 (average value for 1987-1990 years). The influence of the Atlantic Ocean at Thies (located near the coast) could explain the later ending of the rains.
Table 1. Experimental flowering date (FDRS, doy) and estimated values of end of the rains (ER, doy) calculated with the equation 1 for several sites in West Africa.
| Site | Latitude | Longitude | FDRS | Source | ER |
|---|---|---|---|---|---|
| Niono (Mali) | 14deg. 16' N | 5deg. 58' | 2641 | de Ridder, 1979 | 267 |
| N'Tarla (Mali) | 12deg. 35' N | 5deg. 42' W | 285 | Traoré, 1995 | 277 |
| Shika (Nigeria) | 11deg. 12' N | 7deg. 33 E | 289 | Haggar, 1970 | 283 |
| Thies (Senegal) | 14deg. 48' N | 16deg. 57' W | 300 | Dieng et al., 1991 | 258 |
| Ivory Coast | 5deg. N | 1deg. W | 324 | Cesar, 1992 | 323 |
| | | | | | |
| | | |
| 10deg. N | 1deg. W | 300 | 294 |
The onset of the rainy season is extremely variable in West Africa: for several sites in Mali, where rainfall records for 30 years were available, the extreme values for the beginning of the rainy season varied more than 100 days among years. However, flowering of Andropogon gayanus occurred at similar dates regardless the onset of the growth period (de Ridder, 1979; Dieng et al., 1991). Photoperiod has a regulatory effect on flowering date of Andropogon gayanus under different starting dates of the growing season (de Ridder, 1979). However, this photoperiodic regulation is limited: for a very early or very late start of the growing season the flowering date of Andropogon gayanus differed markedly (de Ridder, 1979). For those situations the geographical approach for determining flowering date is not suitable.
Using date of flowering in Andropogon gayanus in relation to date of emergence in Niono, Mali (de Ridder, 1979), the rate of development from emergence to beginning of flowering could be estimated. For that purpose, it was considered that the rate of development in the pre-anthesis period depends linearly on the mean air temperature (TMPA, deg.C) and on the photoperiodically active daylength (DAYLP, h), without interactions between both factors (Robert & Summerfield, 1987). A critical daylength (CRDAYL, h) was also assumed at or below which flowering of Andropogon gayanus accessions depends on temperature, regardless of photoperiod, but beyond this critical daylength development rates increase when photoperiod shortens. This assumption is based on the flowering behaviour of several short day crops in tropical and sub-tropical areas (Hadley et al., 1983) including Andropogon gayanus (de Ridder, 1979).
If the photoperiod exceeds CRDAYL:
DVR1 = A2 + B2 * DAYLP (3)
A2 and B2 are parameters (CROP.DAT file)
If the photoperiod is shorter than CRDAYL:
DVR1 = C2 + D2 * TMPA (2)
C2 = BTD / TUEA
D2 = 1 / TUEA
where, BTD is the base temperature for plant development, a value of 10 deg.C being used in other C4 species (Heemst, 1986) was assumed. TUEA is the thermal sum (deg.C d) from emergence to anthesis (CROP.DAT file). TUEA can be calculated from the de Ridder data.
Using the de Ridder (1979) data set A2 and B2 were optimized (Table 2) with the FSEOPT program (Stol et al., 1992).
Table 2. Optimum parameter values.
| A2 | = | 0.49458 |
| B2 | = | -0.03659 |
| C2 | = | -0.01179 |
| D2 | = | 0.00114 |
| CRDAYL | = | 12.84 |
Simulated data are similar to experimental values, except for the first emergence date (Table 3). Flowering of this emergence date was considerably later than of the following emergences. Other factors in addition to photoperiod and temperature must have caused this response.
Table 3. Experimental and simulated flowering dates (doy) under different emergence dates (doy).
| Emergence date | Date of beginning of flowering | |
|---|---|---|
| Field | Simulated | |
| 115 | 275 | 232 |
| 141 | 251 | 248 |
| 171 | 250 | 256 |
| 198 | 261 | 261 |
| 232 | 283 | 282 |
| 252 | 302 | 303 |
In this study, flowering date data refer to newly emerged crops. In Senegal, no dif ferences were found in the date of flowering of Andropogon gayanus in the first and second year after sowing (Dieng et al., 1991).
The variety of Andropogon gayanus used in this study (de Ridder, 1979) was tridentatus, that appears to have an earlier flowering date than bisquamulatus (Table 1).
If the date of beginning of stem elongation [SEDRS (doy) in the rainy season and SEDDS (doy) in the dry season] and the date when 50 % of the plants have flowered [FDRS (doy) in the rainy season and FDDS (doy) in the dry season] are known for a specific site and variety, the development rate is calculated as follows:
in the rainy season:
before the onset of stem elongation in the sowing year:
DVR1 = SEDVS / (SEDRS - EMERGD) (4a)
before the onset of stem elongation in subsequent years:
DVR1 = SEDVS / (SEDRS - BGRS) (4b)
after the onset of stem elongation in the sowing and subsequent years:
DVR1 = (1 - SEDVS) / (FDRS - SEDRS) (4c)
where, BGRS (doy) is the date of beginning of crop growth in the rainy season and EMERGD (doy) emergence date after sowing (both calculated in the PGCR subroutine, Appendix B)
in the dry season:
before the onset of stem elongation:
VR1 = SEDVS / (SEDDS - BGDS) (5a)
after the onset of stem elongation:
VR1 = (1 -SEDVS)/ (FDDS - SEDDS) (5b)
where, BGDS (d) is the date of starting a new crop cycle in the dry season. SEDVS is a numerical value between 0 and 1, arbitrarily set to 0.35, for the stage of beginning of stem elongation.
The development rate during the post-anthesis phase (DVR2, d-1) is temperature-dependent only. This assumption is used on cereals (van Keulen & Seligman, 1987) and other annual crop (Penning de Vries et al., 1989) models. Time from flowering to harvest of Andropogon gayanus was relatively constant among years in the Brazilian Cerrados (de Andrade et al., 1983), lasting on average 40 days, In a Sudan area of Mali the time from flowering to crop maturity was longer, lasting around two months (Traoré, 1995).
DVR2 = BTD / TUAM + 1 / TUAM * TMPA (6)
TUAM is the thermal sum (deg.C d) from anthesis to crop maturity, a value of 1000 being used (Traoré, 1995). Parameter values are in file CROP.DAT (Appendix C).
In the geographical and direct approaches, while the actual to potential transpiration ratio (TRANSR, -) reduces the rate of development during the pre-anthesis phase, it accelerates it during the post-anthesis phase.
The development rate during the phase of shoot death (SSR, d -1) is also temperature-dependent only.
SSR = BTD / TUMD + 1 / TUMD * TMPA (7)
TUMD is the thermal sum (deg.C d) from crop maturity to complete death of shoot (file CROP.DAT, Appendix C).
In the Sudanian and Guinean savannahs of Ivory Coast (Cesar, 1992) and the Sudanian savannahs of Burkina Faso (Fournier, 1987), mostly comprising of perennial grasses, the main growth cycle starts at the beginning of the regular rains (May to July), maximum aboveground biomass (2500-7000 kg ha -1) is reached at the end of the rainy season (September to November) and seed formation usually occurs at the beginning of the dry season. Burning is a common practice, carried out from the beginning of the dry season onwards (December to April). Between the end of the rainy season and burning a new growing period could start (new tillers appear from the base of the tussocks), if water is available in the rooted soil (Piot, 1968; Monnier, 1968; Cesar, 1992). Growth is assumed to start when light reaches the dormant buds at the base of tussocks, but it could be masked by old dead parts of the plant. After burning (almost the entire aboveground biomass disappears), a new growing period starts. In the Guinean zone, perennial grasses grows continuously until the next rainy season, but in the Sudan zones regrowth is usually small (leaves do not reach more than 5 cm) and growth stops soon because of soil water depletion. Subsequently, green leaves wilt and die and no new regrowth takes place until the next rainy season (Fournier, 1987; Cesar, 1992).
The nitrogen store in the aerial biomass is minimal during the dry season (after burning) and the beginning of the rainy season. Subsequently, the nitrogen store increases, reaches a maximum (15-35 kg ha-1 ) before or around flowering and later decreases due to seed fall, leaching, volatilization or recirculation (Haggar, 1970; Egunjobi, 1974; Breman, 1991; Abbadie, 1984; Traoré, 1995).
Root biomass of perennial grasses varies seasonally. In most of the studies, root biomass continuously increased during the rainy season, achieving a maximum at the beginning of the dry season (post-flowering phase) and later decreased during the dry season (San Jose et al., 1982; Fournier, 1987; Dieng et al., 1991; Cesar, 1992; Traoré, 1995). Data on root biomass were much more variable than on shoots. Root biomass also responded to variable weather conditions among years. Root biomass in the Guinean and Sudanian savannahs decreased to one fourth of their previous values after the drought period of 1971-1973 (Cesar, 1992).
Perenniality is associated with a high rate of root growth relative to that of shoots. Belowground biomass was greater than aboveground biomass in savannahs from Ivory Coast (10 000-20 000 kg ha-1) and Burkina Faso (3000-10 000 kg ha-1). However, in sown grasslands of Andropogon gayanus located in the Sudan-Sahel transition zone (Dieng et al., 1991) and in the Sudan zone (Traoré, 1995), root biomass (3000-5000 kg ha-1) was considerably lower than that of the shoots (10 000-17 000 kg ha-1). It is difficult to explain such considerable differences. In some savannahs, they could be partly caused by the presence of geophyte species with large root systems.
The store of nitrogen in the roots in a Sudanian savannah vegetation varied rapidly throughout the year (Abbadie, 1984) with no clearly defined trend. In Andropogon gayanus grasslands in Mali the maximal nitrogen store occurred during the post-flowering phase (Breman, 1991; Traoré, 1995).
The rate of dry matter production of a perennial grass is determined, on a daily basis, with the equation developed by ten Berge et al. (1994), but the nitrogen content of shoots (ANSH, g shoot nitrogen per m 2 ground surface) is used instead of nitrogen content of leaves. Thus, the maximum rate of dry matter production (MXDMP, kg ha-1 d-1) is calculated from daily incident global radiation (RDD, MJ m-2 d-1) and the amount of nitrogen contained in the shoots with the equation:
MXDMP =10 p ANSH [1 - e-epsil RDD/(p ANSH)] (8)
where p is the initial shoot nitrogen use coefficient (grams of dry matter produced per day and per gram shoot nitrogen), epsil is the initial global radiation use coefficient (grams of dry matter produced per MJ incident global radiation). The parameter p represents the overall efficiency with which shoot nitrogen is used in producing dry matter. Both parameters can be estimated directly from experimental data.
Dry matter production is calculated in the subroutine PGDM (Appendix B).
Limitations to dry matter production:
1) Below a threshold value of the shoot nitrogen concentration (AVNCSH, kg kg-1), dry matter production (DMP, kg ha-1 d-1) is progressively reduced when the shoot nitrogen concentration approaches the minimum shoot nitrogen concentration (MNNCSH, kg nitrogen per kg dry matter) and is null below MNNCSH. This is a physiological limitation: when the level of nitrogen in the shoots falls below a minimum net assimilation becomes negative (van Keulen & Seligman, 1987). Above AVNCSH, the rate of dry matter production is equal to MXDMP.
The minimum and maximum nitrogen concentration in the shoots decrease with plant development because of synthesis of nitrogen-poor components later in the crop's life. If the nitrogen source becomes exhausted, the decrease is even stronger because of intensive nitrogen redistribution. Fig. 1 shows the maximum and minimum concentrations of nitrogen in the aboveground biomass of Andropogon gayanus during the main growth cycle. This figure summarizes data from different areas of West Africa (Oyenuga, 1957; Haggar, 1970; Egunjobi, 1974; Cissé & Breman, 1980; Dieng et al ., 1991; Traoré, 1995). Minimum shoot nitrogen concentration is around 5 g per kg DM until flowering and later decreases to 2-3 g per kg DM. These values of minimum shoot nitrogen concentration are similar to Andropogon gayanus data collected by Breman & De Ridder (1991). Maximum and minimum nitrogen concentrations of Andropogon gayanus, a perennial C4 grass, are also similar to values of annual C4 grasses summarized by Penning de Vries & Van Keulen (1991).
Figure 1. Maximum and minimum shoot nitrogen in Andropogon gayanus.
2) Limited water supply results in a reduction in water use and crop growth (van Keulen & Seligman, 1987). To account for water-limited conditions, the ratio actual to potential transpiration (TRANSR, -) is used as a reduction factor that multiply DMP. Below a threshold value of TRANSR (CRTRCA, -, file CROP.DAT) the rate of dry matter production is set to 0.
3) The growth rate of Andropogon gayanus during the dry season was lower than in the rainy season even under optimum water supply. This behaviour has been attributed to low air humidities at Sotuba, Mali (Krul & Breman, 1991) but also to low minimum temperatures at Thies (Dieng et al. , 1991).
Under laboratory conditions, stomata of Andropogon gayanus showed no response to external air humidity [leaf-air vapour pressure deficit (VPD) from 1 to 4 kPa], but the rate of leaf photosynthesis steadily decreased beyond a value around 2.5-3.0 kPa leaf-air VPD. The leaf water potential also decreased substantially with increasing leaf-air VPD (El-Sharkawy et al. , 1984).
Andropogon gayanus species do not occur in areas where the mean minimum temperature of the coldest month is less than 4.4 deg.C (Bowden, 1964), although it is tolerant to light frost (Singh & Chatterjee, 1968), but no data are available about crop dormancy caused by low temperature.
Table 4 shows monthly values of minimum temperature and VPD of the air during the above mentioned dry seasons of Thies and Sotuba (Kita, a nearby weather station is used instead of Sotuba).
Table 4. Monthly average of daily values of minimum temperature (TMMN, deg.C) and air vapour pressure deficit (VPD, kPa) during the dry season of 1988/89 at Thies and 1978/79 at Kita.
| Site | Thies (Senegal) | Kita (Mali) | ||
|---|---|---|---|---|
| Month | TMMN | VPD | TMMN | VPD |
| November | 18.8 | 2.26 | 18.3 | 2.49 |
| December | 16.3 | 2.66 | 17.3 | 2.90 |
| January | 16.5 | 2.80 | 19.6 | 3.31 |
| February | 18.0 | 2.60 | 22.1 | 3.73 |
| March | 17.8 | 2.66 | 24.3 | 4.60 |
| April | 17.4 | 2.38 | 26.0 | 4.78 |
| May | 19.9 | 2.26 | 27.9 | 4.35 |
| June | 22.5 | 2.02 | 23.4 | 2.00 |
At Sotuba, after burning (January 1979), the aerial living biomass of Andropogon gayanus was slightly higher under optimum water supply (150 kg ha-1 in March and 320 kg ha-1 in June) than without irrigation (60 and 100 kg ha-1, respectively). Crop photosynthesis might be reduced by low values of VPD during the dry season, but not by temperature. From March onwards, minimum temperature was equal to or higher than the optimum flowering temperature (Tompsett, 1976).
At Thies, low night temperatures during the cold dry season could lead to accumulation of starch in leaf chloroplasts and reduced dry matter production. This response to low night temperatures has been described for other C4 species (West, 1973). VPD values are not likely limiting crop growth.
Further insight of growth of perennial grasses during the dry season is needed. It seems difficult to explain the low observed values of aerial biomass through air humidity or temperature limitations only.
The model assumes that low air humidity can reduce dry matter production. A reduction factor (RFCAAH, -), function of air vapour pressure deficit, is introduced to account for this effect. This factor is equal to 1 below a VPD threshold value (CRVPD1, file CROP.DAT) of 3.0 kPa (El-Sharkawy et al., 1984) and from there on decreased linearly till 0 at CRVPD2 (arbitrarily set to 5 kPa, file CROP.DAT). Temperature is not considered to limit growth for West African conditions.
Thus, the actual rate of dry matter production (ADMP, kg ha -1 d-1) is calculated as follows:
ADMP = DMP * MIN(TRANSR, RFCAAH) (9)
The growth rate of the crop consists of two terms: actual dry matter production (ADMP) and non-structural carbohydrates translocated from senescent shoots and roots (Subsection 2.5).
The maximum growth rate calculated for C4 species (most of the perennial grasses of West Africa) is approximately 400 kg ha -1 d-1 (Lövenstein et al., 1992), but the maximum growth rate reported (MXGRCR, file CROP.DAT) of Andropogon gayanus grass was 305 kg ha-1 d-1 (Traoré, 1995). This value is used as maximum growth rate in the current version of the model.
Thus, the actual growth rate of the crop (GRCR, kg ha-1 d-1) is calculated as:
GRCR = MAX[MXGRCR, ADMP + (WCTSH+WCTRT)/ASRQCR)] (10)
where WCTSH (kg ha-1 d-1) and WCTRT (kg ha -1 d-1) are carbohydrates translocated from senescent shoots and roots, respectively, and ASRQCR is the assimilate requirement for dry matter production of the crop.
Under potential growth conditions, partitioning of dry matter between shoot and roots is a function of the stage of plant development. However, biomass allocation may be modified, either by environmental constraints (water shortage and nutrient deficiencies) or by crop management (forage exploitation).
The fraction of dry matter allocated to the shoots (FBSH, -) and roots (FBRT, -) under potential growth conditions has been estimated from experimental data in Mali (Traoré, 1995).
Dry matter partitioning is calculated in the subroutine PGDM (Appendix C).
Under potential growth conditions, the growth rate of shoots (GRSH, kg ha-1 d-1) is calculated as the fraction of dry matter allocated to shoots times actual rate of dry matter production. However, water stress or nitrogen deficiency can modify dry matter partitioning in favour of the roots (Brouwer, 1963; Hamblin, 1987). RFSGWS (-) is a reduction factor introduced to account for water stress effects on shoot growth rate: its value is equal to 1 when the plant water uptake meet the transpirational demand and equal to 0.9 (rather arbitrarly assumed) times TRANSR under water-limited conditions. RFSGND, (-) is a reduction factor accounting for nitrogen deficiency effects on shoot growth rate: if the shoot nitrogen concentration approaches to its minimum value, the value of RFSGND decreases with decreasing nitrogen concentration.
Growth rate of shoots is constrained to the maximum reported value: 260 kg ha-1 d-1 (Egunjobi, 1974) in the pre-anthesis phase and 20 kg ha-1 d-1 (Traoré, 1995) in the post-anthesis phase. The recorded increase in shoot biomass after flowering has been small, zero or negative (Haggar, 1970; Egunjobi, 1974; Breman, 1991; Dieng et al., 1991; Traoré, 1995). Seed production is low and extremely variable: the maximum yield of pure seed recorded was about 350 kg ha-1 but under commercial conditions, seed production is in the range of 65-120 kg ha-1 (Ferguson, 1990).
Under potential growth conditions, the root growth rate (GRRT, kg ha-1 d-1) is calculated as the fraction of dry matter allocated to the roots times actual rate of dry matter production. However, as mentioned before, water stress or nitrogen deficiency can reduce shoot growth and therefore more dry matter can be allocated to the roots.
Root growth temporarily ceases after cutting or grazing and all assimilates are allocated to the shoots (Sibma & Ennik, 1988). This limitation continues as long as the shoot to root ratio is less than a critical value. In the model this critical ratio (SHRTR, -, file CROP.DAT) was set, rather arbitrarily, to 0.5.
Root growth also ceases when the nitrogen concentration in the roots is equal to or below a minimum value. Fig. 2 shows the root nitrogen concentration of Andropogon gayanus at N'Tarla and Cinzana (Traoré, 1995) during the rainy season. No systematic decrease in root nitrogen concentration with plant age was observed as in the shoots. The same behaviour has been reported for annual grasses (Penning de Vries & Van Keulen, 1991). The store of root nitrogen is calculated as a fraction of the total crop nitrogen store. This fraction has been estimated from experimental data in Mali (Traoré, 1995).
Figure 2. Maximum and minimum root nitrogen in Andropogon gayanus.
In the Sudanian savannahs (Fournier, 1987; Cesar, 1992), significant increases in dead shoot biomass start in August-September, the senescence process accelerate strongly after ripening (November) and most of the shoot biomass are dead in December. In Guinean savannahs, shoot senescence is delayed, particularly in wetter areas or years, compared to Sudanian savannahs (Cesar, 1992).
Dynamics of senescence and disappearance of perennial grass roots were described in the Trachypogon savannahs of the High Central Plains of Venezuela (San Jose et al., 1982), an area characterised by a pronounced seasonal rainfall of 1300 mm and a four months dry season. While most of the root biomass was functioning during the first half of the wet season (vegetative growing period), most of it was non-functional during the dry season (functional roots remained constant circa 240 kg ha-1 ). Non-functional root biomass increased during the second half of the rainy season and decreased during the dry season and more rapidly during the beginning of the next wet season.
Measurements of turnover rates of the perennial grass roots (ratio between annual belowground growth and maximum belowground dry matter) suggest that roots may survive on the average for one year: values of 1.08 for an Andropogoneae Guinean savannah (Menaut & Cesar, 1979); 0.85 for Sudanian savannahs (Fournier, 1987), around 1 for a Trachypogon savannah (San Jose et al., 1982) and 0.72 for an Andropogon grassland (Dieng et al., 1991) have been recorded. A root turnover rate of 1 has been assumed in the present model.
PGWA assumes that senescence of perennial grasses starts at the stage of stem elongation (Fournier, 1987; Cesar, 1992). Death rate of shoots and roots is calculated as the relative death rate times the living biomass of shoots and roots, respectively. The relative death rate increases with crop age: for shoots it is calculated in such a way that approximately half of the shoot biomass is dead at crop maturity (Fournier, 1987); for roots it is arbitrarly assumed to be half that of the shoots. Shoot senescence data of Traoré (1995), who reported only 10 % of the aboveground biomass dead in December, are quite different from savannah data (Fournier, 1987; Cesar, 1992).
Water stress accelerates crop death rates (Fournier, 1983). In the current model it is assumed that the relative senescence rate of shoot and roots increases by a factor (WSESSH and WSESRT, -, respectively), defined as a function of the intensity and duration of water stress (Tables WSESST and WSESRT, file CROP.DAT).
Following crop maturity, the remaining living shoots senescence. During that period, the rate of shoot senescence is a function of air temperature and living biomass (Subsection 2.2.2).
Root death continues after crop maturity, but this process is limited by the minimum structural roots needed for reserve storage. As reserves are used, structural roots progressively die.
It is accepted that a minimum level of energy is necessary to ensure plant survival and to start regrowth after complete defoliation or burning of perennial tropical grasses (Humphreys, 1981). Circulation of assimilates and nutrients between above and belowground parts of perennial tropical grasses has been hypothesized (de Rham, 1971; Villecourt et al., 1979; Abbadie, 1984; Cesar, 1992; Breman, 1991) to explain regrowth, either after burning in the dry season (most of the aboveground plant material has disappeared) or at the beginning of the rainy season (most of the aboveground plant material is dead). That could also explain the higher biomass production and nutrient store of perennial grasses in comparison to annual grasses observed in the Sahel zone (Cissé & Breman, 1980). During the last part of the growing cycle, leaves and stems wilt and die. In these processes they loose about half their dry matter (Fournier, 1987; Cesar, 1992); part of this biomass is translocated to the seeds (low demand), but the remainder could be transported to roots and basal parts of stems, where it is stored. At the beginning of regrowth, these reserves are remobilized and used to develop new rooted shoots until the plant is photosynthetically independent. In other natural ecosystems such as tundras, storage and internal recirculation of nutrients are important attributes of perennial plants, enabling them to survive where the availability of nutrients is extremely low (Berendse & Jonasson, 1992).
Some experimental evidence is available to support this hypothesis, e. g. nitrogen translocation from shoots to roots at the end of the growth cycle in the rainy season has been measured in Andropogon gayanus plants (Breman, 1991). However, most of the data used to support the hypothesis on storage and recirculation of carbohydrates and nutrients are indirect indications, such as the dynamics of shoot and root biomass (Fournier, 1987; Cesar, 1992) where seasonal variations in root biomass are often masked by the high variability in observed data (Fournier, 1987; Abbadie, 1984; Cesar, 1992), by the distinction between living and dead roots and by interseasonal variations, e.g. major drought periods, (Cesar, 1992). Further research is needed to determine whether or not the internal recirculation of nutrients and energy exists and to assess the importance of these processes.
PGWA assumes the existence of storage and internal recirculation of energy between above and belowground parts of tropical perennial grasses. Accumulation of carbohydrates takes place in the roots and stem bases of perennial herbaceous grasses (Stoddart et al., 1975). For Andropogon gayanus is assumed to occur in the upper part of the root system, which includes rhizomes and cord roots (94 % of the total dry matter of the roots, Bowden, 1963b), because of the common practice of shoot burning.
Translocation of root reserves for regrowth could start either after an effective rainfall event at the onset of the rainy season or after burning or cutting of dead aboveground biomass in the dry season, if enough water is available in the soil [higher than a threshold value set to 25 mm (CRWARC, mm, CROP.DAT)]. The length of time during which store reserves are being depleted with the onset of growth used to be a few days for grasses (White, 1973). It has been arbitrarily set to 3 and 10 days. for the dry and rainy season, respectively (TCRTDS and TCRTRS, file CROP.DAT). The rate of translocation of reserves is defined as the difference between potential and actual rate of growth and the potential growth rate is calculated with a relative growth rate (RGR, file CROP.DAT). Below a minimum concentration of the reserves, set to 0.05 kg kg-1 (MNCRS, file CROP.DAT), translocation stop.
As vegetative growth proceeds, there is a gradual replenishment of stored carbohydrates (Stoddart et al., 1975). Storage of carbohydrates is assumed to start once translocation of reserves for regrowth has ceased: a fraction of the assimilates allocated to the roots (FRBARS, -, file CROP.DAT) is stored as reserves. Moreover, if production exceeds the demand of carbohydrates, the surplus is also stored as reserves. The concentration of non-structural carbohydrates is limited to a maximum value of 0.3 kg kg-1 (MXCRS, file CROP.DAT) derived from data for lucerne (Versteeg, 1985).
A fraction of non-structural carbohydrates (WCWSH from shoots and WCWRT from roots, kg ha-1 d-1) and nutrients from senescent material may be translocated to other plant parts, where it can be used either for dry matter production or stored as reserves. During the shoot senescence phase (SSS 0 to 1), non-structural carbohydrates are assumed to be used for maintenance respiration of the crop.
Once active growth ceases, stored reserves gradually decline though the dormant period, being used in respiration. That which remains at the end of the dormant season provides the material with which growth begins (Stoddart et al., 1975).
In the Sahel and Sudan zones, the rate of crop growth
during the long
dry season is usually to be low (limited either by water,
nutrients or climatic
conditions). This could be a mechanism for avoiding or delaying
extreme water
stress. Normally, most of available water is depleted soon in the
dry season
and the aerial parts of the plant wilt and die (Cesar, 1992).
Subsequently,
plants remain in dormancy until the first effective rainfall
event triggers
regrowth in the next rainy season. During this dormancy period
plants maintain
perennating buds, that become rooted shoots when conditions are
favourable for
plant growth. The survival mechanisms of tropical perennial
grasses are not
well understood, but it is accepted that perennating bud survival
requires
(Humphreys, 1981):
- maintenance of a root system able to extract soil moisture to
maintain plant
turgor and promote the nutrient uptake needed for bud elongation;
- accumulation of energy to cover respiratory losses during the
dormant periods
and to translocate reserve carbohydrates to activate buds until
they become photosynthetically independent.
These requirements are indirectly supported by experimental data.
- Perennial grasses at the dry limit of their geographical
distribution area, such as Andropogon gayanus in the
Sudan-Sahel transition
subzone and the North Sudan zones disappeared from these regions
after the severe
drought periods of 1969-1973 and 1982-83 (Diarra, 1976; Le
Houérou, 1989). If
the rate of plant water uptake cannot meet the transpiration
requirement to
maintain plant turgor, plant water status declines and the plant
dies when a
critical relative water content (RWC, -) is reached. Sinclair
& Ludlow
(1985) reported that leaves of 27 C4 grasses died at a
average value
of RWC of 25% (s.e. of 1%).
- Andropogon gayanus (var. tridentatus) plants died
under forage
exploitation, but not without exploitation (Cissé &
Breman, 1980).
Defoliation, by reducing the level of energy interception and
hence
carbohydrate production reduces the store of non-structural
carbohydrates in
the plant (Richards & Caldwell, 1985). Subsequently, plant
death can occur
when the energy required for maintenance of the existing biomass
during the
dormant period or for root activity and bud elongation at the
onset of the
growth period is not met.
In PGWA, plant death can be caused either by depletion of available soil water or by exhaustion of non-structural carbohydrate reserves.
Water
Transpirational demand is defined as a function of dry matter production and vapour pressure deficit (Section 8.1.3) in growing periods, and is equal to the cuticular transpiration rate in periods of aboveground biomass senescence (SSS 0 to 1) and plant dormancy. During summer dormancy, the water use of some perennial plants (Agropyrum elongatum, Oryzopsis holciformis, Medicago sativa and Hordeum bulbosum) in the Central Negev Highlands (Israel) was measurable, fluctuating from 0.1 to 0.3 mm d-1 (Tadmor et al., 1966). PGWA assumes that cuticular transpiration rate fluctuates within this range depending on aboveground biomass.
Once available water for transpiration is used, dehydration of plants starts and they die after a few days (TCCDWE equals to 8 d, file CROP.DAT).
Non-structural carbohydrates
During crop dormancy, carbohydrates from reserves are respired to provide energy for maintenance of existing biomass. Maintenance requirements of roots and shoots are calculated using fixed coefficients and taking into account temperature effects (Penning de Vries & Van Laar, 1982). The reference temperature is assumed to be 25 deg.C. Because of the low plant activity during crop dormancy, compared with growth periods, a reduction factor, set to 0.05 (RFMR, file CROP.DAT) is used to estimate actual respiration losses.
Build-up of carbohydrate reserves in roots occurs during the growing periods (Subsection 2.5).
When non-structural carbohydrate reserves are exhausted, some structural plant parts are respired and plants die after a few days (TCCDRE equals to 8 d, file CROP.DAT).
Data on adaptation of Andropogon gayanus to forage exploitation seem conflicting. High forage yields of Andropogon gayanus were observed in a Sudano-sahelian area of Senegal with Atlantic influence (Dieng et al., 1991) and in wetter areas of West Africa (Audrau et al., 1966; Haggar, 1970; Barrault, 1973) and India (Singh & Chatterjee, 1968), with 3-4 cuttings per year and with fertilizer application. No plant mortality was reported. However, a very high plant mortality under light exploitation regimes in the wet season of 1974 was observed in the Sudano-Sahelian zone of Mali (Cissé & Breman, 1980), and the disappearance of Andropogon gayanus and other perennial grasses in the Sudano-Sahelian transition zone has also been attributed to overgrazing (Le Houérou & Guillet, 1985). In these two cases death of Andropogon gayanus plants occurred immediately after the prolonged drought period of 1969-73 in a zone where Andropogoneae perennial grass species are at the drier limit of their geographical distribution. Thus, the observed mortality of already extremely weak plants may be explained by the reduction in the carbohydrate pool caused by the forage exploitation (Caldwell et al., 1981).
PGWA simulates forage exploitation in a simple way, based on the assumption that shoot biomass density is independent of height. The fraction of biomass (living and dead) removed by exploitation is calculated as the difference between height of the crop and height of exploitation (file CROP.DAT). Height of the crop is defined as a function of crop development stage, calculated as the fraction of the maximum height of the canopy (Dieng et al., 1991) times the maximum canopy height. This parameter is equal to 2.5 m in the rainy season (MXHCRS, file CROP.DAT) and 1 m in the dry season (MXHCDS, file CROP.DAT) according to Dieng et al. (1991). The maximum height of the canopy may be lower because of previous exploitation, but water or nutrient effects on height of the canopy are not considered.
As tufted perennial grass species exhibit rapid internode elongation from the onset of stem elongation (around September for Andropogon gayanus), active apical and intercalary meristems are elevated and could therefore be removed by cutting or grazing. When this happens regrowth, that can only proceed after activation of new basal axillary meristems is delayed and reduced (Richards & Caldwell, 1985). In the model, removal of the active meristems by cutting or grazing from the beginning of stem elongation results in the onset of a new vegetative cycle (Audrau et al., 1966) with translocation of carbohydrate reserves from roots to shoots and the death in a few days of most of the living biomass left below the exploitation height.
The Sahel water balance developed by van Keulen (1975) and adapted to the Fortran Simulation Environment, version 2.0, by van Kraalingen (unpublished) is used in the model (Appendix B: DRSAHE.FOR). The soil profile is divided into a maximum number of 10 layers or compartments. Thickness of soil compartments and plant characteristics are inputs to the model (file SOIL.DAT, Appendix C).
Four specific volumetric water contents (cm3 H2 O cm-3 soil) are directly or indirectly (by definition of the texture classification of each layer) required: saturation (WCST), field capacity (WCFC), wilting point (WCWP) and air dry (WCAD)
Run-off occurs when the water supplied (rainfall plus run-on) exceeds the infiltration capacity of the soil and the water accumulated at the soil surface exceeds surface storage capacity. Run-off from a field can be 0-20 % of precipitation and even higher on unfavourable soils or with large and high intensity showers (Stroosnijder & Koné, 1991). Because of lack of detailed information, infiltrated rainfall (INRAIN, mm d-1 ), actual rainfall minus run-off (or plus run-on), is calculated with an empirical relation as a function of daily rainfall (RAIN, mm d-1 ):
INRAIN = MAX [0, RAIN1*(RAIN - RAIN2)] (11)
RAIN1 (-) and RAIN2 (mm d-1) are input parameters (file CROP.DAT)
Redistribution of the infiltrated water is assumed to occur within one day. This is reasonable except in heavy clay soils. Thus, if infiltrated water exceeds the water holding capacity of a layer, the excess drains into the next layer. If more water enters the profile than can be retained at field capacity, the excess drains below the root zone (DRAIN, mm d-1). Waterlogging conditions are not simulated.
Potential evapotranspiration in the Sahel, calculated with Penman's formula, varies from less than 1800 mm yr-1 in the south to over 2200 to the north (Cochemé & Franquin, 1967; Davy et al., 1976). The difference between experimentally measured and calculated values was less than 4% (Riou, 1975). Daily potential evapotranspiration is around 4 mm d-1 in the cool dry season and 6.5 mm d-1 in the hot dry season, with lower values along the ocean shore and higher values in continental areas with strong winds (Le Houérou, 1989)
The Penman-Monteith combination equation (van Laar et al., 1992) is used to calculate potential evapotranspiration (PET, mm d-1 ) in the subroutine PETP (Appendix B).
Water losses through soil evaporation are especially important at the beginning of the rainy season when soil cover is low or absent and soil temperatures are very high. Temperature values between 50-60 deg.C were measured in June in a Sudano-Sahelian area of Mali, Stroosnijder & Koné (1991). Soil evaporation continues, albeit at a decreasing rate, until the soil is air dry. Potential soil evaporation rate (EVSC, mm d-1) is calculated, taking into account shading of the soil surface by living and dead plant material, with an adaptation of Ritchie's equation (1972). An extinction coefficient for the crop canopy of 0.5 (van Laar et al., 1992) and SCC (m 2 m-2), the shading cover of living and dead aboveground biomass, instead of leaf area index are used. SCC is tabulated as a function of total living and dead shoot biomass (Traoré, 1995):
EVSC = PET * EXP(-.5*SCC) (12)
Actual soil evaporation rate (EVSW, mm d-1) is calculated using the formulation of Stroosnijder (1982) in the subroutine DRSAHE (Appendix B). A distinction is made between days with rain and days without rain. In the former case, actual evaporation rate is set equal to the potential, taking into account that the top layer cannot be depleted beyond air-dry water content. For days without rain actual evaporation rate is below the potential and the reduction is approximated using the experimental field observation that cumulative evaporation is proportional to the square root of time after the last rain (Stroosnijder, 1982, 1987). The proportionality factor (mm d-1/2) is assumed to be equal to the product of 0.6 (d-1/2) the potential evaporation rate times the time of integration (van Laar et al., 1992).
The rooted depth (ZRT, m) is defined as the deepest point from which the crop effectively extracts water. The potential elongation rate of roots (PRER, m d-1) is set equal to 0.08, but physical, chemical and biological factors both in the soil and in the crop can lead to lower extension rates (Hamblin, 1987). For annual cereals in temperate and Mediterranean conditions the value of PRER was around 0.02 m d-1 (Gregory et al., 1978; Barraclough & Leigh, 1984). In West African conditions a higher value of 0.03 m d-1 has been assumed (Jansen & Gosseye, 1986) as temperatures are more favourable. A reduction factor (WRFRE, -) is introduced to account for low soil moisture conditions where root tips are located (Salim et al., 1965). WRFRE is assumed to be equal to the water uptake reduction factor for that soil compartment.
The maximum rooting depth (MXRDCC, m) depends on species and cultivar characteristics: on friable soils, rooting depths of Andropogon gayanus of 3 m have been found (Schultze-Kraft, unpublished); this value has been set for MXRDCC. Maximum rooting depth also depends on soil characteristics (MXRDSC, m), as root penetration ceases when an impermeable layer in the profile is reached. Moreover, generally root extension ceases around flowering (Penning de Vries et al., 1989), but if water is present in the layer where root tips are located root extension may continue after flowering (Gregory et al., 1978; McGowan et al., 1984; Bonachela, 1991).
Most of the roots of perennial grasses in West Africa savannahs are concentrated in the upper soil layers (Menaut & Cesar, 1979; Cesar, 1992). In Ivory Coast, cumulative biomass distribution of perennial grass roots (CWRT, kg ha-1) from different savannah types could accurately be described by a negative exponential function (Cesar, 1992). Similar root distribution patterns were observed in Andropogon gayanus in Mali (Breman, 1991; Traoré, 1995). However, the root distribution varied between the rainy and dry season: relatively more roots were found in deeper layers in the dry than in the rainy season (Traoré, 1995).
The model simulates biomass distribution of the roots on the basis of a negative exponential function:
CWRT = WSRT * (1 - eRTDE*DEPTH/ZRT) (13)
where WSRT (kg ha-1) is the structural root biomass, DEPTH (m) is the depth of upper or lower boundary of a layer and RTDE is a parameter describing the extinction coefficient for the exponential function.
A value of RTDE of 5 (-), mean value from different Guinean savannahs (Cesar, 1992), is used during the rainy season. This value gives fair estimates for the root biomass distribution of Andropogon gayanus (Traoré, 1995) at the end of the rainy growing season, but not at the end of the dry growing season (Table 5). For that season, a value of RTDE equal to 3 gives better root distribution estimates.
Table 5. Comparison of cumulative root distribution (%) of Andropogon gayanus within the profile at the end of the rainy growing season (21.1.94) and at the end of the dry season (27.4.94) with simulated values obtained with a negative exponential function using different values of the exponent RTDE. N'Tarla (Mali).
| Measured | Simulated | ||||
|---|---|---|---|---|---|
| Season | rainy | dry |
| ||
| RTDE (-) | 3 | 5 | 6 | ||
| Depth |
| ||||
| 0.0 - 0.2 | 77 | 52 | 45 | 63 | 70 |
| 0.2 - 0.4 | 85 | 68 | 70 | 86 | 91 |
| 0.4 - 0.6 | 92 | 84 | 83 | 94 | 97 |
| 0.6 - 0.8 | 97 | 93 | 91 | 98 | 97 |
| 0.8 - 1.0 | 100 | 100 | 100 | 100 | 100 |
An average value of 100 m g-1 (Siddique et al., 1990) for the specific root dry matter is used to determine root length in each rooted layer (RL, m m-2).
Potential transpiration rate (MTRANS, mm d-1) is determined directly from daily dry matter production, taking into account nitrogen-limited conditions (DMP, g m-2 d-1), but not air humidity conditions (RFCAAH, Subsection 2.3.2).
Tanner & Sinclair (1983) showed that transpirational efficiency of a crop species is conservative over a wide range of conditions. Thus, on a daily basis, the transpiration rate could be estimated as (Amir & Sinclair, 1991):
MTRANS = DMP*VPD/(WUEC*10 (14)
where WUEC is the water-use efficiency coefficient, assumed to be equal to 12 Pa for Andropogon gayanus based on Tanner & Sinclair (1983). Mean daily vapour pressure deficit (VPD, kPa) is estimated from the air water vapour content [vapour pressure early in the morning VP (kPa)] and minimum temperature (TMMN deg.C)] and mean day temperature (MDT, deg.C), defined as (Peake et al., 1978):
MDT = TMMX - 0.25*(TMMX-TMMN) (15)
where TMMX is the maximum daily temperature (deg.C)
Water uptake by the roots depends on maximum transpiration rate (MTRANS) and availability of water in the soil. For a gramineous annual crop rooting density is not, normally, considered a limiting factor for water uptake (van Keulen, 1975). Most available field data, for example for cereal crops, show no relationship between water uptake and root length density (Gregory et al., 1978; Hamblin et al., 1990; Siddique et al. , 1990). In these cases, water uptake is limited by water potential in the rooted soil volume rather than by root density (van Keulen & Seligman, 1987).
However, in some situations this assumption may be not valid. Roots of various species extracted water from the upper half of the soil profile, where most of roots are concentrated, until soil moisture potential fell below -1.5 MPa, but in deeper layers some residual water was left (Jordan & Miller, 1980; Bonachela, 1991). Jordan et al. (1983) suggested that the low root density in the deeper layers could limit water uptake. Moreover, below a root length density of 1 cm cm-3 the residual water in the profile at the end of the growth cycle increased as root length density decreased (Cooper et al., 1987; Bonachela, 1991).
Perennial grass crops usually continue growth during rainless periods even when most of the available water is in the deeper soil layers, where root density is usually very low (Breman, 1991; Dieng et al., 1991; Cesar, 1992). Thus, under those conditions, root density could limit the rate of water uptake and affect the rate of growth and survival of the crop. A maximum transpiration rate per unit of root length (MXTRRL) of 1.25 kg m-1 d -1 (Azam-Ali et al., 1984) is used in the model to account for root density limitations.
The model treats each soil compartment individualy but compensatory water use can occur, i.e. when part of the root system is in dry soil compartments, those parts that are in wetter compartments, will take up more water (cf. Lawlor, 1973).
Water uptake by roots is equal to water demand (MTRANS) under optimum water supply, but decrease below a critical water content (water-limited conditions). The calculation of water uptake capacity under water-limited conditions is based on Doorenbos & Kassam (1979) and Driessen (1986). A critical moisture content (WCCR, cm3 H2O cm-3 soil) per soil compartment is defined that denotes the transition from potential to water-limited conditions, through the soil water depletion fraction (SWDF, -). The value of SWDF depends on species or variety and evaporative demand (Driessen, 1986).
For each soil layer, the following variables are calculated:
critical water content:
WCCR(I) = (1.-SDWF)*(WCFC(I)-WCWP(I))+WCWP(I) (16)
water availability (WACWC, mm d-1) before water stress can occur:
WACWC(I) = MAX(0., (WCLQT(I)-WCCR(I))*TKL(I)*1000) (17)
maximum water extractable by roots (WARL, mm d-1), obtained by multiplying the maximum transpiration rate per unit root length (kg H2O m-1 root d-1) by the root length (RL, m m-2):
WARL(I) = MXTRRL*RL(I) (18)
maximum water availability (mm d-1) taking into account water availability and root length:
MXWA(I) = MIN (WACWC(I), WARL(I)) (19)
a reduction factor for water uptake due to low water availability (-):
RFWU(I) = MIN(1., MAX(0.,(WCLQT-WCWP(I))/(WCCR(I)-WCWP(I)))) (20)
Thus, the rate of water wptake for transpiration for each soil layer is calculated as:
TRWL(I) =MTRANS* ERL(I)/ERLB * RFWU(I) (21)
where ERL is effective root length (m m-2) per layer, that it is equal to the root length (RL, m m-2), and ERLB is the cumulative effective root length (m m-2).
If total available water within the rooted profile is higher than the water demand (MTRANS) water supply is optimal (rate of water uptake from layers where RFWU is equal to 1 is increased to compensate for those layers with RFWU below 1); alternatively the crop is under water stress and the transpiration rate is below the potential.
The sum of TRWL over the profile is actual transpiration (TWE, mm d-1). The ratio between actual and potential transpiration rate is the transpiration ratio (TRANSR, -).
This chapter guides users in running the model and also offers field researchers the possibility to contribute to further development of the model. Many of the model assumptions have been introduced in the form of input parameters in the file CROP.DAT and, therefore, can be easily modified.
A separate description of the Fortran Simulation Environtment (version 2) and the SAHEL water balance is in preparation (contact to D.W.G. van Kraalingen). The description of the WEATHER system (van Kraalingen et al. , 1991), RADIAT and PENMAN (van Kraalingen & Van Keulen, 1987) and the TTUTIL system (Rappoldt & Van Kraalingen, 1990) can be obtained from the AB-TPE group. These modules will therefore not be further treated in this manual.
The PGWA model consists of the following parts:
PGRAIN determines the beginning of the rainy season (DBRR, doy) each year. DBRR tries to simulate the start of regular rains. It is calculated using the Hiernaux (1984) definition: DBRR is defined as the first day with rain equal to or higher than 25 mm (RAIN1, file CROP.DAT) or the first of five consecutive days with accumulated rain equal to or higher than 25 mm (RAIN3, file CROP.DAT). To reduce the effect of very early rains in the year, outside the main rainy season, an additional condition, i.e. that accumulated rain during 20 days (TCRD1, file CROP.DAT) following the possible date of start of the rainy season must be higher than 20 mm (RAIN3, file CROP.DAT), has been included.
Depending on data availability, different approaches can
be used to
simulate crop development (Appendix B:
PGPHE.FOR). Before running
the model,
definition of the integer variables IVPDRS and IVPDDS permit
select one of
them:
- IVPDRS or IVPDDS is equal to 0 when the flowering date is
known; in that case
values have to be assigned to the integer variable FDRS and also
to FDDS
(Appendix A) if occurs flowering in the
dry season.
- IVPDRS is equal to 1 when flowering date is estimated from the
geographical
data of the site (LATS and LONGS). This approach can only be used
in the rainy
season.
- IVPDRS or IVPDDS is equal to 2 when flowering date is estimated
from
daylength and temperature data. In that case site-specific or
accession-specific values have to be assigned to the variables
C2, D2 and TUEA.
IVPDRS or IVPDDS = 0
FDRS : flowering date in rainy season
FDDS : flowering date in the dry season
IVPDRS = 1
LATS : latitude of the simulation site
FDDS : flowering date in the dry season, if present
IVPDRS or IVPDDS = 2
BTD : base temperature for development
TUEA : thermal sum from emergence to anthesis
CRDAYL : critical daylength
For the post-anthesis and crop senescence phases accession-specific values have to be assigned to TUAM and TUMD variables.
Shoot nitrogen data can be introduced as follows:
- as a function of development stage (IVSCNS equals 0);
- as a function of day of the year (IVSCNS equals 1).
Forage exploitation always occurs, by burning (TYPEXP equals 0) or cutting (TYPEXP equals to 1, cutting height can be selected with the variable HEC, m), at the end of the shoot senescence phase in the dry season.
Exploitation of forage may be simulated using either of three
approaches:
- by introducing the date of the first exploitation, the time
interval between
exploitations and their total number;
- by introducing the critical value of aboveground biomass that
triggers
exploitation;
- by introducing the development stages at which exploitation is
triggered.
The selection among these aproaches is governed by the integer
variables EDRS
in the rainy season and EDDS in the dry season:
- EDRS = 0 or EDDS = 0, for the time interval approach;
- EDRS = 1 or EDDS = 1, for the critical biomass approach;
- EDRS = 2 or EDRS = 2, for the critical phenological stage
approach.
The soil water depletion fraction (SWDF) for Andropogon gayanus can be determined from crop groups 4 and 5 (Doorenbos et al., 1978).
Basic input requirements:
TKL : thickness of soil compartments
WCLQT : initial water content
Soil moisture characteristics can be defined by the user:
WCAD : water content at air dry;
WCWP : water content at wilting point;
WCFC : water content at field capacity;
WCST : water content at saturation.
For more information about the DRSAHE water balance contact to D.W.G. van Kraalingen (AB-DLO).
In the TIMER.DAT file (Appendix C) an
integer variable
(IVSSC) has been
introduced to select the starting point of the model (there are
two possible
ways of running PGWA):
- if IVSSC is equal to 0 the model simulates several subsequent
growing years
from sowing; sowing date (SD) must be defined in file CROP.DAT.
- if IVSSC is equal to 1 the model simulates several subsequent
years from an
established crop; initial values of aboveground (IWSH) and
belowground biomass
(IWRT) and rooting depth (IZRT) must be defined in the CROP.DAT
file.
The model must always start before the beginning of the rainy
season (parameter
DAYB).
Basic input requirements
DAYB : start day of simulation
FINTIM : last day of simulation
IYEAR : start year of simulation
WTRDIR : directory of weather data
CNTR : country code of weather data
ISTN : station number of weather data
IVSSC : Selection of the starting point of the model
IVSSC = 0
SD : sowing date
IVSSC = 1
IWSH : initial shoot weight
IWRT : initial root weight
IZRT : initial rooting depth
This file is used to name the timer, crop, and soil files.
Abbadie, L., 1984. Evolution saisonnière du stock d'azote dans la strate herbacée d'une savane soumise au feu en Côte d'Ivoire. Acta Oecol. Oecol. Plant. 5: 321-334.
Amezquita, M.C., E.A. Pizarro & J.M. Toledo, 1990. Range of adaptation of Andropogon gayanus. In: Toledo J.M., R. Vera, C. Lascano & J.M. Lenne (Eds.), Andropogon gayanus Kunth, a grass for tropical acid soils. Centro Internacional de Agricultura Tropical (CIAT), Cali. p. 37-64.
Amir, J. & T.R. Sinclair, 1991. A model of water limitation on spring wheat growth and yield. Field Crops Res. 28: 59-69.
Andrade R.P. de, D. Thomas & J.E. Ferguson, 1983. Seed production of pasture species in a tropical savanna region of Brazil. II. Grasses. Trop. Grassl. 17: 59-64.
Audrau, J., G. Lamarque, J.P. Lebrun & R. Rivière, 1966. Emsembles pastoraux du Logone et du Moyen Chari (République du Tchad), I.E.M.V.T., Mont Pellier, 210 pp.
Azam-Ali, S.N., P.G. Gregory & J.L. Monteith, 1984. Effects of planting density on water use and productivity of pearl millet ( Pennisetum typhoides) grown on stored water. I. Growth of roots and shoots. Exp. Agric. 20: 203-214.
Barraclough, P.H. & R.A. Leigh, 1984. The growth and activity of winter wheat roots in the field: the effect of sowing date and type of soil on root growth of high yielding crops. J. Agric. Sci. Camb. 103: 59-74.
Barrault, J., 1973. La recherche fourragère au Nord-Cameroun. Production et valeur alimentaire de quelques fourrages locaux (travaux menés par l'IRAT de 1965 à 1971). Agron. Trop. 28: 173-188.
Berendse, F. & S. Jonasson, 1992. Nutrient use and nutrient cycling in northern ecosystems. In: Chapin, F.S., R.L. Jefferies & J.R. Reynolds (Eds.), Arctic ecosystems in a changing climate. An ecophysiological perspective. Academic Press, San Diego. p. 337-356.
Berge, H.F.M. ten, M.C.S. Wopereis, J.J.M. Riethoven, T.M. Thiyagarajan & R. Sivasamy, 1994. The ORYZA_0 model applied to optimize nitrogen use in rice. In: Berge, H.F.M. ten M.C.S. Wopereis & J.C. Shin (Eds.), SARP Research Proceedings, Wageningen. p. 235-253.
Bie, S. de, 1991. Wildlife resources of the West African savannas. Ph. D. Thesis, Wageningen Agricultural University, Wageningen. p. 51-83.
Bogdan, A.V., 1977. Tropical pasture and fodder plants (grasses and legumes). Tropical Agricultural Series, Longman, London & New York. 475 pp.
Bonachela, S., 1991. Caracterizacion de los cereales de invierno en la provincia de Granada (sistema radical, uso del agua y productividad). Doble aprovechamiento (forraje + grano). Ph. D. Thesis, Córdoba. 210 pp.
Bowden, B.N., 1963a. Studies on Andropogon gayanus Kunth. I. The use of Andropogon gayanus in agriculture. Emp. J. Exp. Agric. 31: 267-273.
Bowden, B.N., 1963b. The root distribution of Andropogon gayanus var. bisquamulatus. East Afr. Agric. For. J. 29: 157-159.
Bowden, B.N., 1964. Studies on Andropogon gayanus Kunth. III. An outline of its biology. J. Ecol. 52: 255-271.
Breman, H., 1991. La productivité des herbes pérennes et des arbres. In: Penning de Vries, F.W.T. & M.A. Djitèye (Eds.), La productivité des pâturages sahéliens, Agric. Res. Rep. 918, Pudoc, Wageningen. p. 284-295.
Breman, H. & N. de Ridder, 1991. Manual sur les pâturages des pays sahéliens. Karthala, ACCT, CABO-DLO et CTA, Wageningen. 485 pp.
Breman, H., I.B. Cissé & M.A. Djitèye, 1991. Exploitation, dégradation et désertification. In: Penning de Vries, F.W.T. & M.A. Djitèye (eds; 2nd edition), La productivité des pâturages sahéliens. Une étude des sols, des végétations et de l'exploitation de cette ressource naturelle. Agric. Res. Rep. 918. Pudoc, Wageningen. p. 382-384.
Brouwer, R., 1963. Some aspects of the equilibrium between overground and belowground plant parts. Meded. Inst. Biol. Scheikd. Onderz. Landb. gewass. 231: 31-39.
Caldwell, M.M., J.H. Richards, D.A. Johnson, R.S. Nowak & R.S. Dzurec, 1981. Coping with herbivory: Photosynthesis capacity and resource allocation in two semiarid Agropyron bunchgrasses. Oecologia, Berl. 59: 178-184.
Cesar, J., 1992. La production biologique des savanes de Côte-d'Ivoire et son utilisation par l'homme. Biomasse, valeur pastorale et production fourragère. IVMVT. 575 pp.
Cissé, M.I. & H. Breman, 1980. Influence de l'exploitation sur un pâturage à Andropogon gayanus Kunth var. tridentatus. Rev. Elev. Med. Vet. Pays Trop. 33: 407-416.
Clayton, W.D., 1972. Gramineae. In: Herpper, F.N. (Ed.), Flora of West tropical Africa, vol. III, part 2, 2nd ed., Crown agents for overseas governments and administration, London. p. 349-512.
Cochemé, J. & P. Franquin, 1967. Etude agroclimatologique de l'Afrique sèche au Sud du Sahara en Afrique Occidentale. Projet conjoint OMN/UNESCO/FAO, FAO, Rome. 325 pp.
Cooper, P.J.M., P.J. Gregory, J.D.H. Keatinge & S.C. Brown, 1987. Effects of fertilizer, variety and location on barley production under rainfed conditions in Northern Syria. 2. Soil water dynamics and crop water use. Field Crop Res. 16: 67-84.
Davy, E.G., F. Mattei & S.I. Salomom, 1976. An evaluation of climate and water resources for development of agriculture in the Sudano-Sahelian zone of West Africa. Especial environmental report 9, WMO, no. 459, WMO Geneva. 289 pp.
Diarra, L., 1976. Composition floristique et productivité des pâturages soudano-sahéliens sous une pluviosité annuelle moyenne de 1100 à 400 mm. Thesis, Bamako. 95 pp.
Dieng, A. 1991. Introduction de la culture fourragère temporaire d'Andropogon gayanus Kunth var. bisquamulatus dans la fermette intensifiée du bassin arachidier sénégalais. Ph. D. Thesis, Gembloux. 201 pp.
Dieng, A., A. Buldgen & R. Compere, 1991. La culture fourragère temporaire d'Andropogon gayanus Kunth var. bisquamulatus en zone soudano-sahélienne sénégalaise. 1. Systématique, morphologie, dispersion et biologie de la variété cultivée. Bull. Rech. Gembloux 26: 279-296.
Doorenbos J. & A.H. Kassam, 1979. Yield response to water. FAO Irrigation and Drainage paper no. 33, F.A.O., Rome. 193 pp.
Doorenbos J., A.H. Kassam, C. Bentvelder & G. Uittenbogaard, 1978. Yield response to water. U.N. Economic Commission West Asia, Rome.
Driessen, P.M., 1986. The water balance of the soil. In: H. van Keulen & J. Wolf (Eds.), Modelling of Agricultural Production: weather, soils and crops. Simulation Monographs, Pudoc, Wageningen. p. 76-116.
Egunjobi, J.K.,1974. Dry matter, nitrogen and mineral element distribution in an unburnt savanna during the year. Oecol. Plant. 9, 1-10.
El-Sharkawy, M.A., J.H. Cock, A.A. Held, 1984. Water use efficiency of cassava. II. Differing sensitivity of stomata to air humidity in cassva and other warm-climate species. Crop Sci. 24: 503-507.
Ferguson, J.E. 1990. Seed production of Andropogon gayanus. In: J.M. Toledo, R. Vera, C. Lascano & J.M. Lenne (Eds.), Andropogon gayanus Kunth, a grass for tropical acid soils, Centro Internacional de Agricultura Tropical (CIAT), Cali. p. 277-302.
Foster, W.H., 1962. Investigations preliminary to the production of cultivars of Andropogon gayanus. Euphytica 11: 46-52.
Fournier, A., 1983. Analyse démographique appliquée aux feuilles de quatre espèces de graminées de savane (Côte-d'Ivoire). Acta Oecol. Oecol. Plant. 4: 183-203.
Fournier, A., 1987. Cycle saisonnier de la phytomasse et de la production herbacée dans les savanes soudaniennes de Nazinga (Burkina Faso). Comparaison avec d'autres savanes ouest-africaines. Bull. Ecol. 18: 409-430.
Gregory, P.J., M. McGowan, P.V. Biscoe & B. Hunter, 1978. Water relations of winter wheat. I. Growth of the root system. J. Agric. Sci. Camb. 91: 91-102.
Grof, B. & D. Thomas, 1990. The agronomy of Andropogon gayanus. In: J.M.Toledo, R. Vera, C. Lascano & J.M. Lenne (Eds.) Andropogon gayanus Kunth, a grass for tropical acid soils, Centro Internacional de Agricultura Tropical (CIAT), Cali. p. 157-178.
Hadley, P., E.H. Roberts & R.J. Summerfield, 1983. A quantitative model of reproductive development in cowpea [Vigna unguiculata (L) Walp.] in relation to photoperiod and temperature, and implications for screening germplasm. Ann. Bot. 51: 531-543.
Haggar, R.J., 1970. Seasonal production of Andropogon gayanus . I. Seasonal changes in yield components and chemical composition. J. Agric. Sci., Camb. 74: 487-494.
Hamblin, A.P., 1987. The influence of soil structure on water movement, crop root growth and water uptake. Adv. Agron. 38: 94-157.
Hamblin, A.P., D. Tennat & M.W. Perry, 1990. The cost of strees. Dry matter partitioning changes with seasonal suply of water and nitrogen in dryland wheat. Plant and Soil 122: 47-58.
Heemst, H.D.J. van, 1986. Crop phenology and dry matter distribution. In: H. van Keulen & J. Wolf (Eds.), Modelling of Agricultural Production: weather, soils and crops. Simulation Monographs, Pudoc, Wageningen. p. 27-40.
Hiernaux, P., 1984. Distribution des pluies et production herbacée au Sahel: une méthode empirique pour caractériser la distribution des précipitations journalières et ses effets sur la production herbacée, Premiers résultats acquis dans le Sahel Malien. CIPEA, Bamako.
Humphreys, L.R., 1981. Environmental adaptation of tropical pasture plants, McMillan, London. 258 pp.
Jansen, D.M. & P. Gosseye, 1986. Simulation of growth of millet (Pennisetum americanum) as influenced by water stress. Simulation Reports CABO-TT no. 10, Wageningen. 108 pp.
Jones, C.A., 1979. The potential of Andropogon gayanus Kunth in the Oxisol and Ultisol savannas of tropical America. Herbage Abstr. 49: 1-8.
Jordan, W.R & F.R. Miller, 1980. Genetic variability in sorghum root systems. Implications for drought tolerance. In: N.C. Turner & P.J. Kramer (Eds), adptations of plants to water and hight temperature stresses. Wiley, New York. p. 383-399.
Jordan, W.R., W.A. Dugas Jr. & P.J. Shouse, 1983. Strategies for crop improvement for drought prone regions. Agric. Water Manag. 7, 281-299.
Keller-Grein, G. & R. Schultze-Kraft, 1990. Botanical description and natural distribution of Andropogon gayanus. In: J.M. Toledo, R. Vera, C. Lascano & J.M. Lenne (Eds.), Andropogon gayanus Kunth, a grass for tropical acid soils. Centro Internacional de Agricultura Tropical (CIAT), Cali. p. 1-18.
Keulen, H. van, 1975. Simulation of water use and herbage growth in arid regions. Simulation Monographs, Pudoc, Wageningen. 176 pp.
Keulen, H. van & N.G. Seligman, 1987. Simulation of water use, nitrogen nutrition and growth of a spring wheat crop. Simulation monographs, Pudoc, Wageningen. 310 pp.
Kowal, J.M. & A.H. Kassam, 1978. Agricultural ecology of savanna. A study of West Africa. Oxford University Press, Oxford. p. 69-78.
Kraalingen, D.W.G. van & H. van Keulen, 1987. Model development and applications for the 'Project pilote en agrometeorologie'. CABO-DLO, Wageningen. 166 pp.
Kraalingen, D.W.G. van, W. Stol, P.W.J. Uithol & M. Verbeek, 1991. User Manual of CABO/TPE Weather System. CABO/TPE internal comunications, 27 pp.
Krul, J.M. & H. Breman, 1991. L'influence du feu. In: F.W.T. Penning de Vries & M.A. Djitèye (Eds.) La productivité des pâturages sahéliens, Agric. Res. Rep. 918, Pudoc, Wageningen. p. 356-352.
Laar, H.H. van, J. Goudriaan & H. van Keulen, 1992. Simulation of crop growth for potential and water-limited productions situations (As applied to spring wheat). Simulation Reports CABO-TT no. 27, Wageningen. 72 pp.
Lawlor, D.W., 1973. Growth and water absortion of wheat with part of the roots at different water potentials. New Phytol. 72: 297-305.
Le Houérou, H.N., 1989. The grazing land ecosystems of the African Sahel. Ecological Studies 75, Springer-Verlag, Heidelberg, p. 65-123.
Le Houérou, H.N., 1993. Grasslands of the Sahel. In: R.T. Coupland (Ed.), Ecosystems of the world 8b, Natural grasslands, Elsevier, Amsterdam, p. 197-220.
Le Houérou, H.N. & H. Guillet, 1985. Conservation versus desertization in African arid lands. Proc. 2nd Int. Conf. Conservation Biology, Univ. of Michigan, Ann-Arbor. p. 441-461.
Le Houérou, H.N. & G.F. Popov, 1981. An ecoclimatic investigation of inter-tropical Africa. Plant Prod. Protec. Pap. 31, FAO, Rome. 40 pp.
Lövenstein. H., E.A. Lantinga, R. Rabbinge & H. van Keulen, 1992. Principles of theoretical production ecology, Wageningen Agricultural University. p. 36.
McGowan, M., P. Blanch, P.G. Gregory & D. Haycock, 1984. Water relations of winter wheat. The root system and osmotic adjustment in relation to crop evaporation. J. Agric. Sci. Camb. 102: 415-425.
Mejía-M., M., 1984. Andropogon gayanus Kunth: bibliografía analítica. Centro Internacional de Agricultura Tropical (CIAT), Cali. 196 pp.
Menaut, J.C. & J. Cesar, 1979. Structure and primary production of Lamto savannas, Ivory Coast. Ecol. 60: 1197-1210.
Miles, J.W. & B. Grof, 1990. Genetics and plant breeding of Andropogon gayanus. In: J.M. Toledo, R. Vera, C. Lascano & J.M. Lenne (Eds.), Andropogon gayanus Kunth, a grass for tropical acid soils. Centro Internacional de Agricultura Tropical (CIAT), Cali. p. 19-36.
Monniaux, G., 1978. Structure génétique des populations naturelles d'Andropogon gayanus Kunth au Sénégal. Office de la Recherche Scientifique et Technique d'Outre-Mer (ORSTOM), Dakar. 103 pp.
Monnier, Y., 1968. Les effets des feux de brousse sur une savane préforestière de Côte-d'Ivoire. Abidjan, Ets. éburnéennes no. IX, 260 pp.
Oyenuga, V.A., 1957. The composition and agricultural value of some grass species in Nigeria, Emp. J. Exp. Agric. 25: 237-255.
Peake, D.C.I., E.F. Henzell & G.B. Stirk, 1978. Simulation of herbage production and soil water use by biloela buffel grass in small plot experiments at Narayen. Tropical Agronomy, CSIRO, St. Lucia, Brisbane, Queensland.
Penning de Vries, F.W.T & H. van Keulen, 1991. La production actual et l'action de l'azote et du phosphore. In: F.W.T. Penning de Vries & M.A. Djitèye (Eds.), La productivité des pâturages sahéliens, Agric. Res. Rep. 918, Pudoc, Wageningen. p. 196-225.
Penning de Vries, F.W.T & H.H. van Laar, 1982. Simulation of plant growth and crop production. Simulation Monographs, Pudoc, Wageningen. 308 pp.
Penning de Vries, F.W.T., D.M. Jansen, H.F.M. ten Berge & A. Bakema, 1989. Simulation of ecophysiological processes of growth in several annual crops. Simulation Monographs 29, Pudoc, Wageningen. 291 pp.
Piot, J., 1968. Végétaux ligneux et pâturage des savanes de l'Adamaoua. Maison-Alfort, IEMVT, Wakwa, CRZ. 41 pp.
Rappoldt, C. & D.W.G. van Kraalingen, 1990. Reference manual of the FORTRAN utility library TTUTIL with applications. Simulation Reports CABO-TT no. 20, Wageningen. 122 pp.
Rham, P. de, 1971. L'azote dans quelques forêts, savanes et terrains de culture d'Afrique tropicale humide (Côte-d'Ivoire). Ph. D. Thesis, Lausanne. 124 pp.
Richards, J.H. & M.M., Caldwell, 1985. Soluble carbohydrates, concurrent photosynthesis and efficiency in regrowth following defoliation: a field study with Agropyron species. J. App. Ecol. 22: 907-920.
Ridder, N. de 1979. Fotoperiodiciteit in the Sahel. een zaaitijdenproef met ruim 20 in Sahel voorkomende plantesoorten en een model voor de berekening van bloeidatum van deze soorten. PPS, Wageningen. 27 pp.
Riou, C., 1975. La détermination pratique de l'évaporation. Application à l'Afrique Centrale. Mém 80, ORSTOM, Paris. 236 pp.
Ritchie, J.T., 1972. Model for predicting evapotranspiration from a row crop with incomplete cover. Water Resour. Res. 8: 1208-1213.
Robert, E.H. & R.J. Summerfield, 1987. Measurement and prediction of flowering in annual crops. In: F.G. Atherton (Ed.), Manipulation of Flowering, Proc. of the 45th Easter School, Faculty of Agricultural Science, Univ. of Nottingham. London, Butterwoths, p.17-50.
Salim, M.H., G.W. Todd & A.M. Schlehuber, 1965. Root development of wheat, oats and barley under conditions of soil moisture stress. Agron. J. 57: 603-607.
San Jose, J.J., F. Berrade & J. Ramirez, 1982. Seasonal changes of growth, mortality and disappearance of belowground root biomass in the Trachypogon savanna grass. Ecol. Plant. 3, 17: 347-358.
Sibma, L. & G.C. Ennik, 1988. Ontwikkeling en groei van produktiegrass onder Nederlandse omstandigheden. Pudoc, Wageningen. 53 pp.
Siddique, K.H.M., R.K. Belford & D. Tennant, 1990. Root:shoot ratios of old and modern, tall and semi-dwarf wheats in a mediterranean environment. Plant and Soil 121:89-98.
Sinclair, T.R. & M.M. Ludlow, 1985. Who taught plants thermodynamics? The unfulfilled potential of plant water potential. Aust. J. Plant Physiol. 12: 213-217.
Singh, R.D. & B.N. Chatterjee, 1968. Growth analysis of perennial grasses in tropical India. I. Herbage growth in pure grass swards. Exp. Agric. 4: 117-125.
Spain, J.M. & W. Couto, 1990. Establishment and initial development of Andropogon gayanus. In: J.M. Toledo, R. Vera, C. Lascano & J.M. Lenne (Eds.), Andropogon gayanus Kunth, a grass for tropical acid soils. Centro Internacional de Agricultura Tropical (CIAT), Cali. p. 222-246.
Stoddart, L.A., A.H. Smith & T.W. Box, 1975. Range management, McGraw-Hill series in Forest resources, New York, p.104-146.
Stol, W., D.I. Rouse, D.W.G. van Kraalingen & O. Klepper, 1992. FSEOPT a FORTRAN program for calibration and uncertainty analysis of simulation models. Simulation Reports CABO-TT no. 24, Wageningen. 24 pp.
Stroosnijder, L., 1882. Simulation of the soil water balance. In: F.W.T. Penning de Vries & H.H. van Laar (Eds.), Simulation of plant growth and crop production. Simulation Monographs, Pudoc, Wageningen. p. 175-193.
Stroosnijder, L., 1987. Soil evaporation: test of a practical approach under semi-arid conditions. Neth. J. Agric. Sci. 35: 417-426.
Stroosnijder, L. & D. Koné, 1991. Le bilan d'eau du sol. In: Penning de Vries, F.W.T. & M.A. Djitèye (Eds.), La productivité des pâturages sahéliens, Agric. Res. Rep. 918, Pudoc, Wageningen. p. 133-165.
Tadmor, N.H., O.P. Cohen, L. Shanan & M. Evenari, 1966. Moisture use of pasture plants in desert environment. Paper submitted to the X International Grassland Congress, Helsinki. 24 pp.
Taerum, R., 1970. Comparative shoot and root growth studies on six grasses in Kenya. E. Afr. Agric. For. J. 36: 94.
Tanner, C.B. & R.T. Sinclair, 1983. Efficient water use in crop production: research or re-research? In: H.M. Taylor, W.R. Jordan & T.R. Sinclair (Eds.), Limitations to efficient water use in crop production. ASA, Madison. p. 1-27.
Tompsett, P.B., 1976. Factors affecting the flowering of Andropogon gayanus Kunth: responses to photoperiod, temperature and growth regulators. Ann. Bot. 40: 695-705.
Traoré, M., 1995. Utilisation des éléments nutritifs par une graminée pérenne : Andropogon gayanu. Thèse 3ème cycle, ISFRA, Bamako (in press).
Versteeg, M.N., 1985. Factors influencing the productivity of irrigated crops in Southern Peru, in relation to prediction by simulation models. Ph. D. Thesis, Wageningen Agricultural University, Wageningen. 182 pp.
Villecourt, P., W. Smidt & J. Cesar, 1979. Recherches sur la composition chimique (N,P,K) de la strate herbacée de la savane de Lamto (Cote d'Ivoire). Rev. Ecol. Biol. Sol. 16: 9-15.
West, S.H., 1973. Carbohydrate metabolism and photosynthesis of tropical grasses subjected to low temperatures. In: R.O. Slatyer (Ed.), Plant response to climatic factors. Proc. Uppsala Symp., 1970, Unesco, Paris. p. 165-168.
White, L.M., 1973. Carbohydrate reserves of grasses. a review. J. Range Manage. 26: 13-18.
Zimmer, A.H., D.M. Pimentel, C.B. do Valla & N.F. Seiffert, 1983. Aspestos práticos na formaçaode pastagens. Technical circular no. 12, Empresa Brasileira de Pesquisa agropecuária (EMBRAPA) at the Centro Nacional de Pesquisa de Gado Corte (CNPGC) Campo Grande.
