SÉRIE TERRA E AGUA

DO INSTITUTO NACIONAL DE INVESTIGAQAO AGRONOMICA

C O M U N I C A Q A O . N o . 49

Potent ia! Pasture Product iv i ty and

L ivestock Carry ing Capaci ty

Over Mozambique

J.R. T i m b e r l a k e

S . J e e v a n a n d a R e d d y

1 9 8 6 Maputo, Mocambique

ISRIC U3RARY

SERIE TERRA E AGÜA Do Instituto Nacional de Investigacao Agroooiica

Coaunicacao No. 49

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POTENTIAL PASTÜHE PHODÜCTIVITY AND LIVESTOCK CARRYIN6 CAPACITY OVER MOZAMBIQUE

J.R. Tiaberlake Range Survey/Livestock Production Expert

&

S. Jeevananda Reddy Land Resources Agro-ecology/Agro-cliaatology Expert

Deceiber, 1986 Maputo, Mozambique

\is(>\

Table of Contents

Page No.

Title page i Table of Contents iii List of Tables iv List of Figures iV Smaraary V

INTRODUCTION 1

PRIMARY PASTURE PRODÜCTIVITY 2

2.1 Data used 2 2.2 Methodology 2

2.2.1 Coaputation of rainfall at different 2 probabilities

2.2.2 coaputation of total dry aatter 6 2.2.3 Napping of TDM 7

LIVESTOCK CARRYING CAPACITY 7

3.1 Deteraination of livestock carrying capacity 8 3.1.1 Pasture priaary productivity 8 3.1.2 Density of tree and bush cover 8 3.1.3 Percentage use 9 3.1.4 Dry aatter intake 9

3.2 Carrying capacity calculations 12

DISCUSSION 17

4.1 Priaary productivity 17 4.2 Tree and shrub cover 19 4.3 Cosparison of results 19 4.4 Linitations of aodel 22

4.4.1 Soil fertility and drainage 22 4.4.2 Rainfall infiltration 22 4.4.3 Species coaposition 23 4.4.4 Water distribution 23 4.4.5 Burning 23 4.4.6 Resting 24

CONCLUSIONS 24

REFERENCES 26

in

List of Tables

Protein requireients for cattle at different 11 weights and productivity levels

Dry Matter intake of cattle at different weights 11 and productivity levels on the basis of crude protein requireients

Dry Matter intake for cattle at 6% crude protein 12 level

Calculated carrying capacities of various 13 livestock areas

Maxiaui effective rains and potential TDM 18 production for given soil AWC

Recorded pasture productivity data from 20 Mozambique coapared to estimated values fros •odel of Reddy & Timberlake

List of Figures

Distribution of cli«atic stations used 3 in study over Mozambique

Distribution of lean annual precipitation 4 over Mozambique

Distribution of ieao annual potential 5 evapotranspiration over Mozambique

Potential priiary pasture productivity back over Mozambique at 25% probability

Potential primary pasture productivity back over Mozambique at 50% probability

Potential primary pasture productivity back over Mozambique at 75% probability

Effect of tree and shrub cover on pasture 10 TDM productivity

IV

SUMMARY

A sinplified growth siaulatioo model presented earlier, which uses annual climatic data inputs (rainfall and potential evapotranspiration) and soil available water holding capacity, was used to estisate potential primary production values froi cleared pastures for 134 stations over Mozambique. This was presented in the forn of maps at three rainfall probability levels.

A nodel was developed which can utilize the pasture productivity data, reductions due to tree and shrub cover utilization factors and dry matter intake of the animals, to calculate the livestock carrying capacity of a given area. Examples are presented from various representative livestock areas in Mozambique, both in the commercial and family sectors.

Major conclusions are that moisture availability is probably limiting for pasture production in the south of the country but less so in the north, where soil fertility is more likely to be limiting. The density of woody cover appears to be an important factor in reducing pasture production, pointing out the importance of bush control.

Data on this subject is very scarce in Mozambique, but the results of the study seem to be reasonable. Further field data should be collected in order to verify the models.

V

1. INTRODUCTION

Livestock production on the basis of extensive grazing of runinants on natural pastures is an inportant fora of agricultural activity in nany parts of Mozanbique, principally south of the Hio Save. Many faraers in these areas practice a •ixed fora of agriculture, cultivating crops often using draught power, and building up capital in the f o n of cattle. The natural resources and potentials for extensive grazing of ruainants in Mozanbique are large and still relatively underexploited, although there are some severe constraints in aany areas in the north of the country such as woodland cover, poorer quality grasses, tsetse fly and lack of a tradition of cattle raising.

The aajor requirement for successful livestock raising on natural pasture is not to exceed the livestock carrying capacity of the area, that is the nunber of aninals the area can support in the long teria. If this capacity is exceeded overgrazing results which can lead to environnental degradation such as soil erosion and a drastic reduction in carrying capacity. Therefore there is a necessity to know the potential carrying capacities of livestock areas, both those presently utilized and those proposed for future production. Unfortunately there is very little data available on pasture productivity or livestock carrying capacity fron Hozaabique, and only soie data froii neighbouring countries can be extrapolated to situations here due to clinatic differences. Mozanbique does have however good coverage of clinatic data, both in geographical distribution of stations and in nunber of years of data. This good coverage has been nade use of in the present study.

In seni-arid environnents in particular prinary pasture production is closely linked with available noisture (O'Conner, 1985) and the length of growing season (McCown, 1973). A nodel developed in Australia by Rosé et al. (1972) successfully sinulated prinary productivity of a pasture legune using soil noisture budgeting, that is a function of the anount and distribution of rainfall, potential evapotranspirat ion and the anount of noisture that can be stored in the soil. This nodel was sinplified by Reddy & Tinberlake (1985) to use annual clinatic data and was adjusted for Mozanbican conditions on the basis of pasture productivity data fron Zinbabwe (Oye & Spear, 1982).

This study presents naps of potential pasture productivity over Mozanbique using the nodel of Reddy & Tinberlake (1985) at three levels of probability: 75* [the value that can be expected in, on average, three out of four years], 50* [two out of four years] and 25* [one out of four years]. The deternination of livestock carrying capacity fron prinary production data is discussed using a series of additional factors such as tree and shrub cover, percentage utilization of annual

1

dry aatter production, and variable animal feed intake. In thit, study only cattle, the major forn of livestock in the country, are considered although with adaptations the aodel could be used for other ruiioants. Exaaples of carrying capacity froi various present and potential livestock areas are given.

2. PRIMARY PASTURE PRODUCTIVITY

Priaary pasture productivity as used here is defined as the total dry aatter production (TDM) that can be expected from natural cleared grassland under rainfed conditions with no fertilizer or special aanageraent practices. It is noraally aeasured in tons per hectare per year.

2.1 Data used

This study uses available annual precipitation data froa 134 locations in Mozambique [Fig. 1], both means (R) and standard deviations (/j ). Length of data from stations used varies between 15 and 70 years. Distribution of raean annual precipitation over Mozaabique is shown in Fig. 2. Nean annual potential evapotranspiration values (PB) are calculated using the aethod of Penaan (1948) as adapted by Frere (1978) using a correction factor (Heddy, 1984). The distribution of potential evapotranspiration over Mozaabique is shown in Fig. 3. The other data used is the available water holding capacity (AWC) of the top 1.8 a of the soil profile (Reddy & Veraeer, 1984), based on the 1:2 aillion soil aap of Mozaabique (Voortaan & Spiers, 1981).

2.2 Methodology

The aethodology used to determine TDM production is that of Reddy & Tiaberlake (1985), and is presented in brief below. It is in two parts, the first part dealing with the estimation of precipitation at the different probability levels and the second part dealing with the actual coaputation of TDM.

2.2.1 Coaputation of rainfall at different probabi1ities

Many extensive grazing areas in Mozaabique are characterized by a highly fluctuating rainfall from year to year, and this annual variation should be taken into account while calculating long—term carrying capacities.

Rao et al. (1972) and Griffiths (1967) found that the seasonal and annual rainfall totals follow oormal distribution. The square root of aonthly totals also show noraality (Griffiths, 1967). Thus using the noraal distribution function the annual precipitation aaounts at different levels of probabiIities are estiaated froa the aean and standard deviation as follows:

2

32» T

38° 4 0°f — I -

42°

SCALE j. 1:4000.000

Pwnbo

FIG.1 DISTRIBUTION OF CLIMATIC STATIONS USED IN STUDY OVER

MOZAMBIQUE

LEGEND

Pafur . Meteorological stations

'Inharréane

Moomba

Chongolane ^ r » *

Source-. Reddy ( 1984 )

J I

1 2 ° -

16° -

22«H

S2*«-

_L Des: R Mac iel

32° r 34°

36° —r

38° 1.0'E

— I — 4?°

SCALE j. 1:4000.000

12° •

HG. 2 DISTRIBUTION OF MEAN ANNUAL , „ . -, i ^ *» PRECIPITATION OVER MOZAMBIQUE

LEGEND

900 Mean annual raintall (mm).

S 2 4 ° -

26°-

Source. Reddy ( 1984 )

DesP Moeit

32° 34° r 36°

38° iO°£ —T" 4?°

SCALE i 1:3000.000

FIG. 3 DISTRIBUTION OF MEAN ANNUAL 20° POTENTIAL EVAPOTRANSPIRATION CVER

MOZAMBIOUE

LEGEND

2 2 ° -ïsoo— Mean annual potential evapotranspirtion

(mm).

S24°

Source. Reddyt 1984 )

_L Des: P MaeM

R R 50 *P R ± t 50+ p (M) (U

where R aeao annual rainfall, BBI

= [ Ej Rj J/n, in which Rj is the annual rainfall of year i and n is the nusber of years for which the annual rainfall data are available

= Standard deviation of annual rainfall, BIB

[ T (R-R;r )/n]'

•50±p t-value at 50 ± p percent. probability level

R 50±p estifflated annual precipitation at 50 ± p percent probability level, mm

R, expected annual rainfall at probability P, ram

if R

with R 50

50 +P

R

then R50+D » but if R+t5Q_p then R50-P

In this study the three probability levels refer to 25%, 5 0 * and 7 5 * of years. For exaiple, 75% refers to the expected rainfall in 3 out of 4 years, therefore the TDM values will be equal to or «ore than the 75% value in 3 out of 4 years.

2.2.2 CoMputation of total dry Matter

TDM is calculated for all 134 locations using the siaplified si«ulation model of Reddy & Tinberlake (1985). The procedure in brief is:

Step 1: Coipute effective rainfall (R'n ) from expected rainfall

(R, ) and potential evapotranspiration (PE) for the three probability [P] levels:

R'r [Rp/PEJ x 1800 [2]

Step 2: Using' the effective rainfall (R' ) and AWC coipute actual evapotranspirat ion [AE J for the three probability levels [ P J :

AE,

RO,

R'r R0r

[ 1 5 . 2 4 8 2 / K 0 ' 8 J [R ' p / 1 0 0 J 3

[3]

[4]

where R', annual effective rainfall at probability P (mm)

6

Hp = expected annual rainfall at probability P (BIB) estiaated in 2-2.1

ROn = annual runoff at probability P ( n )

AEp = annual actual evapotranspiration at probability P (EIE)

K - available water holding capacity of soil (EUB)

Step 3: Using the calculated AE values total dry natter production [TDMp ] estiaates for the three probability levels under zero applied superphosphate levels are calculated:

if AEp < 29 mm:

TDMp = 0 [5)

if 29 v< AE p ^ 263 BH:

TDMp = (3.32(AEp 29)J[1.613 ±

0.613(F-1/125)I/4] [6]

if AEp > 263 mm:

TDM p = [777 + 6.26(AEp 263)][1.613 ±

0.613(F-1 /125)'*] [7]

if F - .1 > 0 then ± is positive, or if F - 1 < 0 then ± is negative.

where TDMp = potential pasture dry natter production (t/ha/year)

AEn - actual evapotranspirat ion (an)

2.2.3 Mapping of TDM

superphosphate fertilizer level (kg/ha)

Calculated potential pasture production values for the 134 locations are shown as Haps in Figures 4, 5 and 6. While drawing the boundaries of the different mapping units first preference was given to AWC boundaries, secondly to PE boundaries and thirdly to rainfall boundaries.

3. LIVESTOCK CARRYING CAPACITY

Pptential livestock carrying capacity is defined as the

7

potential nvuiber of Animal Units [1] that a given area of pasture land can support on a sustainable basis, that is without destroying the natural resources (vegetation, soil, etc.)- It is a potential figure and in reality could be lower if there is poor grazing distribution e.g. because of poor distribution of water. Potential carrying capacity aust be clearly differentiated from stocking rate. Stocking rate is the nunber of Animal Units on a given area, independent of whether this can be sustained in the long ter». An area is correctly stocked when the stocking rate more or less equals the potential carrying capacity.

3.1 Determination of iivestock carrying capacity

To calculate the potential carrying capacity of a given area four factors have to be considered:

1. pasture primary productivity 2. density of tree and bush cover 3. percentage of pasture utilizable on a sustainable

basis 4. dry matter intake per aai taal per year

These factors are discussed separately below. The other major factor, nutritive vaTue of the pasture, is incorporated in this study under the dry matter intake of the animal.

3.1.1 Pasture primary productivity

The method used for calculation of pasture primary productivity is explained in section 2.2 and the results are shown in Figs. 4, 5 & 6 and Table 2. The value shown represents total dry matter production (t/ha/year) from open grassy areas under rainfed conditions.

In this study the 75* probability figure is used, that is the potential production figure that can be expected in, on average, three out of four years. Therefore the area will often have an actual grazing capacity greater than its calculated potential, but this allows for periodic droughts without serious overgrazing or having to resort to de—stocking. It is possible that the 75% figure is too conservative for norraal family sector grazing patterns as in drier years the family sector often utilize areas further away. This figure however gives a conservative idea of long—term potentials without excessive cattle movement.

3.1.2 Density of tree and bush cover

High bush cover can have a large effect on grass production due to coapetition for water (Walker et al., 1981) and

[1] 1 Animal Unit (AU) = 450 kg mature anioal or equivalent

8

sunlight. Although woody vegetation can be utilized by livestock it is generally considered undesirable in pastures, especially when it f o n s dense stands reducing grass bioaass by a factor of three to ten (Donaldson & Kelk, 1970). Blair-Rains & Kassas (1979) give a table (shown graphically in Figure 7) of percentage cover of tree, shrub, and tree + shrub cover and its effect on grass productivity. These values are used in the present study as no infornation on response to clearing is available for Mozambique.

3.1.3 Percentage use

The aaount of the annual priiaary productivity that can be utilized by livestock varies according to the botanical composition of the pasture and to the anieal production levels required. It is essential however to ensure that sufficiënt grass is left both as a reserve for the dry season when the grass is not growing, and to allow recovery to previous productivity levels when the rains return. Utilization should be on a sustainable basis so that (a) the total productivity does not decrease over the years, (b) the species present do not change so as to aake the pasture of lower nutritive value, and (c) the soil is not increasingly exposed to erosion. A rough approxiaation of percentage utilization (the percentage of total seasonal production that can be utilized on a sustainable basis) is 50% (Sweet, 1978). Houerou & Hoste (1977) however suggest a figure of 70% of above-ground bionass during the rainy season and 30% in the dry season (usually averaging 40% utilization over the whole year).

For sweet or nixed pastures the 50% utilization value is used in the present study, but for sour pastures an average figure of 30% is thought more appropriate due to the auch lower palatability of these grasses in the dry season.

3.1.4 Dry aatter intake

Dry aatter [DM] intake (kg DM/head/year) obviously varies with the amount and quality of forage on offer. Very low DM intake figures are reflected in very low, or even negative, liveweight gain [LWG] values. DM intake can be calculated on the basis of energy requireaents or on the basis of protein requireaents. In this study protein requireaents are used. These requireaents vary depending on the weight of the aniaial and the productivity levels to be achieved. Using the data froai the National Acadeny of Sciences study (NRC, 1984, aediua fraaie heifer calves section), the protein requireaents are as in Table 1.

9

Fig. 7 EFFECT OF WOODY COVER ON PASTURE PRODUCTIVITY

12CH

8 0 9 0 lOO

Percentage cover

Souroe : Bloip Roins & Kassam, 1979

10 'Des: J.J. Tamele . P E D / 8 6 0 7 8 '

Table 1: Protein requireients (ga/day) for cattle at differept weights and productivity levels.

Weight Liveweight gaip/day (gm)

(kg) 0 100 200 300 400

250 280 350 421 465 505

350 350 450 508 555 591

450 420 500 588 630 669

Source: NRC (1984)

Table 2: Dry natter intake (kg DM/day) of cattle at different weights and productivity levels on the basis of crude protein requireaents.

Weight Liveweight gain/day (gin)

(kg) 0 100 200 300 400

i] 4% crude protein

250 7.00 8.75 350 8.75 11.25 450 10.50 12.50

ii] 6X crude protein

10.53 11.63 12.63 12.70 13.88 14.78 14.70 15.75 16.73

250 4.67 5.83 7.02 7.75 8.42 350 5.83 7.50 8.47 9.25 9.85 450 7.00 8.33 9.80 10.50 11.15

iii] 8* crude protein

250 3.50 4.38 5.26 5.81 6.31 350 4.38 5.63 6.35 6.94 7.39 450 5.25 6.25 7.35 7.88 8.36

ivj 10% crude protein

250 2. 80 3 50 4. 21 4 65 5 05 350 3 50 4 50 5 08 5 55 5 91 450 4. 20 5 00 5. 88 6 30 6 69

Source: Calculated from NRC (1984) data

11

Dry Matter intake for grazing" aniaals will of course depend on the protein levels of the grasses, and this varies through the year. These daily DM intake values are giveD in Table 2 for grass protein contents of 4, 6, 8 and . 10% crude protein. For the sake of simplicity the 6% crude protein figure, considered as an average over the year, is used in this study.

Various specific examples of feed intake are given in Table 3.

Tab Ie 3: Dry matter intake for cattle at 6% crude protein level.

kg aninal daily LWG Feed requireaent

kg DM/day t/yr

250 100 5.83 2.13 350 100 7.50 2.74 350 200 8.47 3.09 450 200 9.80 3.58 450 400 11.15 4.07

Source: Calculated from NRC (1984) data

This gives a daily feed intake of between 2.1 and 2.5 % of body weight, an acceptable figure.

For the livestock carrying capacity calculations ït was decided to use a DM intake of 2.74 t/year for family sector cattle (350 kg weight), 2.13 t/year for family sector cattle in Tete and Angonia (250 kg weight) which are smaller than the Landim, and 3.58 t/year for cattle on coMercial ranches (450 kg weight) which are oriented towards higher liveweight gains.

3.2 Carrying capacity calculations

Using" the above factors and assumptions calculations of carrying capacities for 11 areas under different grazing systeas and eovirooiental conditions were carried out. These are for important livestock areas or areas with good potentials. Descriptions of the areas and systens with their calculated carrying capacities are given below and suamarized results in Table 4. Differentiation is made between carrying capacities for the heavier aniaals of commercial ranches with their higher daily liveweight gains, and those aniaals of the family sector with lower productivity levels.

t gm)

12

Table 4: Calculat ed carrying capacities of various 1 ivestock areas.

ain

fall (m

m)

1

et io

n

1

a/yr

) [

J p

rod

uc

tio

n 1

d

sh

rub

2 o

E 3*= *- 0 c int

S E 2 o> o Oi 4> o t-

ali

ty

n a

nm

AW

C (

a *

g 5S

Q. 4» .

X t 3 O L.

'S ** o> tili

za

t

ma

tte

7yr)

o c •o

AU

(Z

]

ü a — .§ ** ° 4, > 3 >, o --o a> '5 _̂ e 3 O c 1 o 0» o E UI a @ ö~ 'D O e •o ^ • ^ JZ JZ

Changalane 717 150 2900 70 50 3. 58 0.28 3.5

Moaaba 592 150 1700 70 50 2. 74 0.22 5.9

Chokwe aj regadio 656 200 3000 100 50 2. 74 0.55 2.3 b] sequeiro 656 80 2500 70 50 2. 74 0.32 4.0 c] integrat ed 656 80 2500 70 50 2. 20 0.40 3.2

Pafuri 360 40 700 25 50 2. 30 0.04 34.2

Panda 720 200 2900 50 30 2. 50 0.17 7.5

Urrongas 731 100 3000 70 30 2. 74 0.23 5.6

Rio Buzi 723 80 3200 70 50 3. 58 0.31 3.2

Te te 627 150 2000 25 50 1. 70 0.15 12.2

Angonia 930 160 3500 ,110 30 1. 70 0.68 2.6

Queliaaoe 1441 50 2100 35 30 3. 58 0.06 16.2

Nanpula 1139 100 3100 50 30 3. 58 0.13 7.7

1) according to Blair-Rains & Kassas (1979) 2) 1 AU = 1 nature aninal of 450 kg liveweight

13

Changalane: total pasture production is obtained froa Table 4 and the 75% probability figure used. Bush encroachment is a problea and a Bean of 20% tree and shrub cover is assuaed. The calculation is also carried out for more heavily bush encroached areas with 40% cover. Pastures are sweet/mixed so 50% utilization is used. Dry matter intake is assuraed to be 3.58 t/year for a 450 kg animal, although the calculations are also carried out for 350 kg family sector animals.

pasture production at 75% 20% tree and bush cover [70% of total productionj 50% utilization DM intake of 3580 kg/yr [coaiercial, SC] DM intake of 2740 kg/yr [family sector, SF]

or

or

2900 kg/ha/year 2030

1015 0.28 head/ha 3,5 ha/AU 0.37 head/ha 3.5 ha/AU

If bush encroachment is 40% then only 35% of expected priaary production can be achieved. This will give, if the other variables remain the same, a commercial carrying capacity of 7.1 ha/AU.

Moamba: a heavily grazed family sector livestock area with only some use of erop residues. Bush encroachment can be a problen.

pasture production 20% tree and shrub cover [70% of potential] 50% utilization DM intake of 2740 kg

1700 kg/ha/yr 1190

595 0.22 head/ha

or 4.6 ha/head or 5.9 ha/AU

Chokwe: this is calculated for family sector cattle on heavier soils of the valley with a higher soil AWC (a) the

(although the effects of irrigation are not included), lighter soils area out of the valley and (c) for the system which utilizes erop residues in the dry season.

(b) on the integrated

a] heavy soils:

pasture production 0% shrub cover 50% utilization DM intake of 2740 kg

or or

3000 kg/ha/yr 3000 1500 0.55 head/ha 1.8 ha/head 2.3 ha/AU

this is for the open grassy "planices" only.

U

b] lighter soils:

pasture production 20% tree and shrub cover [70% of potential] 50% utilization DN intake of 2740 kg

2500 kg/ha/yr 1750

875 0.32 head/ha

or 3.1 ha/head or 4.0 ha/AU

areas

this is for the slightly higher, sandy loan, bushed

c] integrated systes:

pasture production 20% tree and shrub cover [70% of potential] 50% utilization DM intake of 2200 kg [rest is erop residuesj

2500 kg/ha/yr 1750

875 0.40 head/ha

or 2.5 ha/head or 3.2 ha/AÜ

this assumes access by the aninals to cropped land in the dry season to feed off erop residues, but the area required is not included in the calculations.

Paf uri: a very dry zone dominated by Colophospenui savanna (xanate). Availability of drinking water is probably an important limiting factor in addition to the relatively low pasture productivity. Xanate is a useful source of browse in the dry season, but the shallow rooted nature of the tree will reduce grass production.

pasture production 50% tree and shrub cover [25% of potential] 50% utilization DM intake of 2300 kg [rest is xanate browse)

700 kg/ha/yr 175

88 0.04 head/ha

or 26.3 ha/head or 34.2 ha/AU

Panda (S. Inhaiabane): a reasonably well-watered zone but mostly with sandy soils and soiewhat sour pastures. Lot of integrated farraing so erop residues probably play an important role in animal nutrition.

pasture production 30% tree and shrub cover [50% of potential] 30% utilization [sour pastures] DM intake of 2500 kg [some use of erop residues]

2900 kg/ha/yr 1450

435

0.17 head/ha or 5.7 ha/head or 7.5 ha/AU

15

Urrongas (N. Inhambane): an area of calcareous and seii-sour pastures with good potential for cattle if problens of saline water and tsetse can be overcome. At present cattle are not found here.

pasture production 3000 t/ha/yr 20* tree and shrub cover 2100 [70% of potential] 30* utilization 630 DM intake of 2740 kg (family sector) 0.23 head/ha

or 4.3 ha/head or 5.6 ha/AÜ

Hio Buzt (Huchevej: area presently not auch utilized for cattle production but with good potential for cooaercial ranching. Bush encroachnent problens are likely to be similar to Changalane.

pasture production 3200 kg/ha/yr 20* tree and shrub cover 2240 [70* of potential] 50* utilization 1120 DM intake of 3580 kg (commercial) 0.31 head/ha

or 3.2 ha/AÜ

Tete: a dry area, principally of goat production. Family sector cattle, which are small, obtain a proportion of their forage fron browsing on xanate.

pasture production 2000 kg/ha/yr 50* tree and shrub cover [25* of potential] 50* utilization [sweet pastures] DM intake of 1700 kg (rest from xanate)

Angonia: arable agricultural area with sour or sour mixed pastures. Lot of use of erop residues, but very little bush encroachment. Calculations do not include area of arable land required to provide erop residues.

pasture production 3500 kg/ha/yr 5* tree cover [110* of potential] 3850 30* utilization [sour pastures] 1155 " DM intake of 1700 kg 0.68 head/ha [rest is erop residues] or 1.5 ha/head

or 2.6 ha/AÜ

500 rt

250 ••

0. 15 head/ha or 6. 8 ha/head or 12.2 ha/AU

16

Quelioane: cattle coHsercially raised in association with coconut plantations on sour-oixed pastures.

pasture production 2100 kg/ha/yr 60% tree cover [35% of potential) 735 30* utilization 221 DM intake of 3580 kg 0.06 head/ha

or 16.2 ha/AU

Waapula: coQEiercial cattle product ion on sour pastures. Iiaportant bush encroachnent probleas.

pasture production 3100 kg/ha/yr 30% tree and shrub cover 1550 " [50% of potential] 30% utilization 465 DM intake of 3580 kg 0.13 head/ha

or 7.7 ha/AÜ

4. DISCUSSION

4.1 Primary productivity

The results of potential pricaary productivity of cleared pastures as shown in Figs. 4, 5 & 6 indicate that the different factors affecting production vary according to the cliaatic zones of the country. The estimated values of pasture productivity vary from 0.5 to 5.0 t/ha/year.

The north of Mozaobique [Niassa, Cabo Delgado, Naapula, Zaabezia and north of Tete Province] has a reasonably reliable and high rainfall [mostly around 1000-1200 mm]. Potential TDM productivity is relatively uniform in space and time, except in isolated pockets of low soil AWC, and differences in rainfall are not well reflected in differences in production, e.g. Lichinga and Zambezia highlands. Thus it appears that moisture availability is not a sajor limiting factor for pasture production, except on very low AWC soils, and soil fertility is probably of greater iaportance. This phenomenon is explained further in Table 5 which shows the maximum effective rainfall and TDM at a given soil AWC. For example 50 mra of AWC soils reach a maximum TDM production when the effective rainfall is 710 mm and actual evapotranspiration is 470 mm. Rainfall higher than 710 mm on these soils is not reflected in increased TDM as the rainfall lost through runoff and deep percolation.

The southern [Maputo, Gaza and Inharabane Provinces] and coastal beits of Mozaobique, along with drier southern part of Tete Province, have loser [nostly between 400 to 700 mm] and less reliable rainfall, varying sarkedly from year to year. The AWC of the soils is also very variable [from 50 to 250 mm], and so TDM production is very varied in this zone. The differences between the 25% and 75% probability production figures is also more marked than in the north. Moisture availability appears a

17

major limiting factor for TDM production as can be seen froi the marked decrease towards the west of Gaza corresponding to the reduction in rainfall, and the obvious differences in productivity on soils of differing AWC. The better moisture storage of higher AWC soils (such as in valleys or associated with the Linbombo hills) ' is reflected in a nuch higher productivity potential.

4.2 Tree and shrub cover

The reductions in TDM production, and threfore in carrying capacity, due to tree and shrub cover are very substantial, and are often larger than differences due to rainfall or soil AWC. The calculated reductions based on the values given in Figure 7 (Blaii—Rains & Kassam, 1979) seen high especially over the 20 to 40% canopy cover range, however Walker et al (1971) shows a similar hyperbolic relationship in Australia. The effect of bush cover is also not uniform as woody cover has a more marked reduction effect on grass production in sandveld areas (0'Connor, 1985), and the effect of low rainfall on grass yields is especially seen in areas with high bush infestation (Rutherford, 1978). These are not accounted for in Fig. 7. Care needs to be taken in measuring or assuming tree and shrub cover as small differences have large effects on the resulting carrying capacity figures. For exaiple in the calculated example from Changalane an increase in bush cover from 20 to 40% results in a halving of the carrying capacity froa 3.5 to 7.1 ha/AU. This emphasises the importance of bush control, although it appears that benefits of bush clearing under 10% cover would not be justified in terras of greatly increased grass production. Bush cover is the only factor used in the calculations that can be nanipulated by the manager.

4.3 Comparison of results

No systematic work has previously been carried out in Mozambique on relating pasture productivity to rainfall or soil type. However a survey of the literature gives some productivity values from various localities in the south of the country. These are shown in Table 7 with calculated dry weights (only fresh weights were given in most of the original studies) and estiraated TDM production for the period of study based on the present model. The values from the four Rio Save sites are raeans based on 36 quadrats each and are thus probably more accurate than the ad hoc samplings from other areas. The Massingir values are also based on means but of fewer samples. As so little infornation was given in the original papers conclusions must be very tentative, but it appears that the relatively reliable Rio Save data corresponds quite closely with estinates using the productivity model. The recorded Massingir data is remarkably low and it can only be assuraed that the sites were on lithosols or refer to areas of poor grass cover under trees. Some of the results from areas such as Mazeminhama, Bela Vista and Zitundo seem anomalous and could be from partly denuded or bushed areas. Data from Chobela from the "aananga" area are similar to expected

19

Table 6. Recorded pasture productivity data from Mozambique compared to estimated values from pasture productivity model of Reddy and Tlmberlake

Location Date Rainfall Recorded Calculated Estimated Estimated

(mm/ Ö t u d y

period) fresh weight dry weight DM production annual DM Soil Estimated Pasture type Ref.

(mm/ Ö t u d y

period) (t/ha) (t/ha)1 from model production type soil AWC

(t/ha/study period)

from model (mm) (t/ha/study period) (t/ha)»

Rio Save A 3.70 (317)J 4.14 1.04 1.0 1.5 sandy 50 Urochloa A

Rio Save B 1.70 (380) 3 4.33 1.X38 1.2 1.8 sandy 50 Urochloa/Heteropogon •

Mopane A

Rio Save C 1.70 (317)3 3.39 0.85 1.0 1.9 sandy 70 Digitaria/Urochloa + A

Rio Save D 1.70 (317)3 3.82 0.95 1.0 1.9 sandy 70

Mopane Urochloa/Digitaria A

Changalane/ Mazeminhama

12.52 (333) 4.95 1.24 1.1 2.7 basalt loams 150 Themeda/Acacia B

Catuane 12.52 333 4.48 1.12 1.1 2.7 basalt loams 150 Themeda/ Acac ia B

Goba/Changalane 12.52 (333) 3.77 0.94 1.1 2.7 basalt loams 150 Themeda/Acacia B

Mazeminhama 11.70 212 7.31 1.83 0.6 2.6 basalt loams 150 Themeda/Acacia (not burnt)

B

Mazeminhama 12.70 275 10.08 2.52 0.7 2.6 basalt loams 150 Themeda Acacia B

O (not burnt)

O Bela Vista 11.70 140 3 0.34 0.08 0.4 1.5 sandy 100 Andropogon

(poor stand, not burnt) B Andropogon

(poor stand, not burnt) Zitundo 11.70 174 0.44 0.11 0.5 2.0 ?sand 50 Themeda/Salacia

(not burnt) B

Zitundo 12.70 282

4

0.57 0.14 0.7 2.0 ?sand 50 Themeda/Salacia (not burnt)

B

Massingir 4.72 697 9.00 2.25 3.0 3.7 allüvial/ fluvial 200 Panicum/Urochloa C . -valley bottoma

Massingir 4.72 697 * 1.9-7.7 0.5-1.9 2.1 2.1 Rhyolitic lithosols 50 • Schmidtia/Themeda C -hilly areas

Massingir 4.72 697 * 1.0 0.25 • 2.7 3.0 Dark re< or grey 100 Enneapogon/Aristida/ C iandy soil + lithosols Schmidtia—rolling landscape

Massingir 4.72 697 1.5 0.38 2.7 3.0 Unconsolidated red- IOO

dish sands + boulderc; Enneapogon/Schmidtia +Mopane - plateau

Massingir 4.72 697 2.0-4.0 0.5-1.0 2.7 3.0 Unconsolidated red- 100

dish sands + boulders Schmidta/Aristida/ Panicum. No Mopane-plateau

Table 6 (cont.)

Location Date

Rainfall

(mm/study

period)

Recorded

fresh weight

(t/ha)

~Calculated

dry weight

(t/ha)

Estimated

DM production

from model

(t/ha/study

period)

Soil

type

Estimated

soil AWC

(mm) Pasture type Ref.

Chobela 4-5.57 550

Chobela 4-6.58 554

Chobela 4-6.58 732

Chobela 4-6.58 554

Chobela 4-6.58 554

Mazeminhama 9.58 n/a

5.00

3.83

8.17

12.67

13.00

2.16

2.31

3.56

5.32

6.98

3.71

2.5

2.5

3.3

2.5

2.5

Sandy loam

Sandy loam

Sandy loam

Alluvial

Alluvial

Basalt loam

80

80

80

200

200

150

Mananga - Urochloa

Mananga - Urochloa

Mananga - Urochloa

Setaria

Themeda

Themeda

0

D

D

D

D

D

1/ Assumlng 25% dry matter 2/ Assumlng unlmproved fertllity, no slope and 1800 mm potentlal evapotranspiration

3/ 1969/70 was a dry year 4/ Using rainfall data from Chokwé

5/ Actual data

Note: Figures in brackets are estimates

Ref 8: A= Myre & Antao 1972 B= Myre 1971 C= Lousa & Rosa 1973 D = Pinho Norgado

(pers. com.)

values, but the data from alluvial soils are Gauch higher. This is probably due to a higher moisture status of the soil nearer the river and the higher fertility.

The Miami model used to calculate primary productivity of vegetation of the world (Lieth, 1975) has been used in a study for Mozambique by Barreto & Soares (1972). They produced a map of the country according to estiaated productivity of natural vegetation which gave values of 0.8 to 2.0 t/ha/yr. It was essentially a converted rainfaH sap as they considered that rainfall was limiting at almost all stations. The results frora the present study are of a larger order of magnitude and the pattern of variation in productivity they describe is different froa that in our study.

There is little data in Mozambique with which to coapare the livestock potential carrying capacity predictions of the model. However the carrying capacity calculations given in section 3.2 and Table 4 seèra to be reasonable. For example the accepted carrying capacity of the Changalane area, obtained from many years of ranching experience, is normally accepted to be 3 to 4 ha/AU (Morgado, pers. comm.), which compares favourably with the calculated value of 3.5 ha/AU. The predicted value for Pafuri (34.2 ha/AU) seeras very low, but this is primarily due to the high bush cover assumed.

4.4 Limitations of model

The model has some limitations, particularly concerning the series of assuiptions that have to be made, and some have not been accounted for. The major ones are mentioned below.

4.4.1 Soil fertility and drainage

This study assumes reasonable soil fertility and reasonable drainage, such that nutriënt availability and oxygen for root growth is not severely liraiting plant production. This assuiptioo is not always valid. For example 0'Conner (1985) states that sandy soils in savannas in higher rainfall areas are nutriënt limited rather than aoisture limited, and work carried out by Dye in Zimbabwe (Dye & Spear, 1982; Dye, 1982) shows that pastures on high AWC soils are more responsive to rainfall than those on low AWC soils. Thus in some areas, e.g., pale sands, saline soils and heavy clays, grass product ion will be substantially lower than potential, and on fertile soils it is more. Allowance should be made for this when calcuiating carrying capacities.

4.4.2 Rainfall infiltration

The infiltration into the soil of most of the incident rainfall is assumed in the model, and this is then considered available for plant growth. In areas with poor vegetative cover, or with relatively impermeable or surface-sealed soils, much of

22

the rain will run off and is thus lost for plant growth. The effective rainfall in these situations can be less than half of the incident rainfall. This also occurs on slopes where not all rainfall will infiltrate even if the vegetative cover is good. These factors are not accounted for in the model, but could perhaps be introduced at a later stage if the rainfall infiltration for a particülar soil, slope or vegeta.tion cover is known.

4.4.3 Species composition

It has been assumed that all grass species are rather sifflilar in their efficiency of growth, that is their response to an increase in available moisture terms of TÜM product ion is siinilar. Some evidence to support this can be found in Dye (1984) who determined that the response of TDM prodüction of Heteropogon contortus, Cymbopogon pospischi1ii, Themeda triandra and Hyparrhenia filipendula were siroilar under the same rainfali conditions, although the growth strategies were different. There are also differences between annual and perennial grass species in that annuals usually have a higher above-ground biomass but production periods are usually short and depend on the previous year's seed product ion as well as rainfall in the current year. A cover of annual grasses is less satisfactory than perennial grass cover in controlling soil erosion or allöwing good infiltration of incident rainfall. Different tree and shrub species have different rooting depths and shallow rooted woody species will have a larger depressive effect on grass growth than deepei—rooted species.

4.4.4 Water distribution

Water distribution is probably the most important determining grazing patterns and intensity. Areas closer to water are always much more heavily grazed than those 5 km away, and areas more than 8 to 10 km from water are hardly grazed. This study assumes an even water distribution, and hence grazing. In reality of couse, even in relatively well-watered areas, grazing will not be even, but average carrying capacities can be calculated.

4.4.5 Burning

Large areas of Mozambique are subject to wildfires every year during the dry season which destroy grazing. This problem is particularly acute in the tall grass sourveld associated with miombo woodland, and on the slopes of the Limbombos hills in Maputo and Gaza Provinces. Any calculations of carrying capacity should try to take the risk of burning into account unless adequate control measures can be assumed [i.e. , firebreaks on commercial ranchesj or unless there is a lack of fuel [e.g., in family sector grazing areas due to heavy grazing].

23

4.4.6 Resting

In the coiuuoal grazing systems of the family sector pastures are rarely systenatically rested, although flood plains and depressions are often kept in reserve for dry season use. In couercial grazing systeis oeasures need to be taken to control bush encroachment. The cheapest way to do this is to rest 25% of the total grazing area in any one year, and then burn this at the end of the dry season to destroy bush regrowth. Thus all the grazing area is burnt once in every four years. This 25% reserve can also be used for dry season grazing in the case of drought year. When calculating commercial stocking rates for the whole ranch this 25% reserve should be taken into account (Sweet, 1980), although this is not included in the calculated exaaples.

5. CONCLUSIONS

Using erop growth model presented earlier which uses annual climatic data inputs (annual rainfall and annual potential evapotranspiration) plus soil available water holding capacity, potential pasture primary production values for Mozambique were calculated, using estimated rainfall at three probability levels. These are presented in the form of maps at three probability levels determined from rainfall records.

These maps show a low variability in pasture productivity in the north of the country and differences due to rainfall and soil AWC are not of a large order of magnitude. This corresponds to the known lesser variability in rainfall of the north, and also suggests that water availability is not the major determining factor in pasture productivity in these areas. However the pattern is different in the southern Provinces and southern Tete which shows a marked correlation of pasture production with rainfall differences and increased soil AWC, as well as a greater variation between probability levels. This corresponds to the suggestion that water availability is a major factor limiting pasture production in these areas, and to the known high rainfall variability between years.

Potential livestock carrying capacities can be calculated using these primary pasture productivity data and a series of factors: tree and shrub cover, proper use factor, and dry matter intake of the animals. It appears that tree and shrub cover is of major importance in determining potential carrying capacity, especially in the 20 to 60% canopy cover range. This emphasises the importance of bush control, but also suggests that bush control when canopy cover is under 10 to 20% is unlikely to give large increases in carrying capacity. Livestock carrying capacities are also shown to depend on the weight of the animal and on the productivity levels considered - family sector stocking rates can be higher than those in the commercial sector as expected productivity is much lower.

24

Important considerations that have not been sufficiently covered in the nodel are the effects of soil fertility and drainage on grass growth, productivity differences of various grass species, effects of different tree species (depending on rooting depth), rainfall runoff due to poor infiltration and slope, and rainfall distribution through the year.

The results, it sust be stressed, are preliminary and as yet are not substantiated. Priority should now be given to

a) obtaining field data on primary productivity under known rainfall and soil AWC conditions for various pasture and soil types;

b) obtaining data on the reduction effect on primary productivity of tree and shrub cover by various species and on various soil types;

c) determining nutritive value through the year of various pasture species so as to refine dry matter intake necessities;

d) raapping pasture types over Mozambique and tree and shrub cover.

It is hoped this model will give some idea of productivity potentials for livestóck over Mozambique and an idea of the major limiting factors regarding natural resources, although there is still insufficiënt data to make a livestóck carrying capacity map of Mozambique. Where the major factors can be reasonably quantified for a given area the model can be used, but data on a national level, especially on tree and shrub cover and pasture type, is either not available or too poor in quality at present. Much refinement of the various inputs and their effects needs to be made, but the study could fora the basis for future research in this field.

25

REFERENCES

Barreto, L.S & Soares, F.A. 1972. Carta provisoria da productividade pfimaria liquida dos ecossisteias terrestres de Mocambique. Rev. Cienc. Agron., Lourenco Marques, 5:11-18.

Blair-Rains, A. & Kassam, A.H. 1979. Land resources and aninal production. Consultants working paper 8, FAO/UNFPA Project INT/75/P13, AGLS, FAO, Rome.

Denny, R.P. 1983. Drought and the veld. Zimbabwe Agric. J., 80:169-174.

Donaldson, C.H. & Kelk, D.M. 1970. An investigation of the veld problems of the Molopo area: I. Early findings. Proc. Grassld. Soc. Sth. Afr., 5:50-57.

Dye, P.J. 1982. Predicting veld growth. Zimbabwe Agric. J., 79:21-25.

Dye, P.J. 1983. Prediction of variation in grass growth in a semi-arid induced grassland. Ph.D. Thesis, University of Witwatersrand, Johannesberg.

Dye, P.J. & Spear, P.T. 1982. The effects of bush clearing and rainfall variability on grass yield and conposition in south west Zimbabwe. Zimbabwe J. Agric. Res., 20:103-118.

Frere, M. 1978. A method for the practical application of the Penman formula for the estimation of potential evapotranspiration and evaporation from free water surface. Rome, FAO, AGP, Bcol., 1.

Griffiths, J.F. 1967. Rainfall patterns within the tropics. In: France, P.S., Tromp, S.W. & Weihe, W.H. (eds.), Proc. 3rd Intern. Biometeorol. Congress, Vol.2, Oxford Pergomon Press.

Houerou, H.N. Ie & Hoste, C.H. 1977. Rangeland production and annual rainfall relations in the Mediterranean Basin and in the African Sahelo-Sudanian zone. J. Range Managment, 30:181-189.

Lieth, H. 1975. Modelling the primary productivity of the world. In: Lieth & Whittaker (eds.) Primary Productivity of the Biosphere. Springer-Verlag, New York.

Lousa, N.F. & Rosa, F.N. 1973. Reconhecimentos pascicola a regiao de Massingir. Comunicacoes 91, H A M , Maputo.

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McCown, R.L. 1971. An evaluation of the influence of available soil water storage capacity on growing season length and yield of tropical pastures using siople water balance models. Agric. Meteorol., 11:53-63.

Myre, M. 1971. As pastagens da regiao do Maputo. Memorias no. 3, H A M , Maputo.

Myre, M. & Antao, L.H. 1972. Reconhecimento pascicola do vale do Save. Conunicacoes 75, H A M , Maputo.

NRC 1984. Nutriënt requirements of beef cattle. 6th edition. National Research Council, National Academy of Sciences, National Academy Press, Washington.

O'Conner, T.G. 1985. A synthesis of field experiments concerning the grass layer in the savanna regions of southern Africa. South African Nat. Sci. Progr. Rept. 114, CSIR, Pretoria.

Penman, H.'L. 1948. Natural evaporation from open water, bare soil and grass. Proc. Roy. Soc. London (A), 193:120-146.

Rao, K.N., George, C.J. & Abhyankar, V.P. 1972. Nature of the frequency distribution of Indian rainfall: Monsoon and Annual. Indian J. Meteorol. Geophys., 23:507-514.

Reddy, S.J. 1984. General cliraate of Mozambique. Comunicacao No. 19a, Serie Terra e Agua, INIA, Maputo.

Reddy, S.J. & Vermeer, A.C. 1984. Estimativa da capacidade de agua disponivel dos solos de Mocambique. Nota Tecnica No. 27, Serie Terra e Agua, INIA, Maputo.

Reddy, S.J. & Timberlake, J.R. 1985. A simple method for the estimation of prinary pasture productivity over Mozambique. Agric. For. Meteorol. (in press).

Rosé, C.W., et al. 1972. A simulation model of growth - field relationships for Townsville stylo (Stylosanthes humilis) pasture. Agric. Meteorol., 10:161-183.

Rutherford, M.C. 1978. Primary pasture production ecology in southern Africa. In: Werger, W.J.A. (ed.), Biogeography and ecology of southern Africa, pp621—659, Junk, The Hague.

Sweet, R.J. 1978. Capacidade de carga. Mimeo notas, DINAP, Maputo.

Sweet, R.J. 1980. Programa de maneio de pastagens extensivas en Mocambique. Final report of Range Management Specialist, FAO MOZ/75/008, Maputo.

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Voortman, R.L. & Spiers, B. 1981. Soil resources inventory of Mozambique. Field Docuieot 45, MOZ/75/011, Maputo.

Walker, B.H., et al. 1981. Stability of semi-arid savanna grazing systems. J. Ecol., 69:473-498.

Walker, J., Moore, R.M. & Robertson, J.A. 1971. Herbage response to tree and shrub thinning in Eucalyptus Populnea shrub woodlands. Aust. J. Agric. Res., 23:405-410.

2 8

ft

1