Carbon, Nitrogen and Phosphorus Budget

in Land Management Systems involving

Acacia senegal in drylands of North

Kordofan, Sudan: Flow and Balances

� Bashir Awad El Tahir

� &

� Jonas Ardo

Introduction

� Dry lands:

� can be defined by aridity index ( the ratio of precipitation to Potential Evapotranspiraton (P/PET):

1.

� Land degradation:

� Definition: the process of progressive deterioration of biological

(flora and fauna) and physical (soil, water, micro-climate etc.)

resources of the land, leading to declineing productivity and

sustainable yields (Singh, 1995)

� Predominant environmental problems:

(1) Degradation of vegetation

(2) Depletion of soil nutrients

Decreased SOM leads to:

� Increased degradation soil physical structure

� increased susceptibility to wind erosion

� creation of active dunes

� Increased run-off & water erosion in fine textured soils

Sudan Drylands

Drylands: The largest in Africa (1.6 m. km2). About (63%) of the total area Where (82%) of the population live.

Land degradation:Nearly 75 million ha (45%) -degraded severely to very severely in the recent past.

Most degraded zones:Arid and semi-arid (74% of total), where 76% of population lives.

Main causesWind erosion main cause in the arid zoneWater erosion dominant cause in the semi-arid zones.

Land Degradation & Ecosystems instability

in Sudan

� Agroecosystems in central Sudan had been characterized by a significant amount of land degradation and conversion during the last decades.

� Causes:

a) Overgrazing ( 30 m ha. (47%) of the total degraded areas.

b) Clearing for various uses (22 m ha)

C) Cultivation in absence of external nutrient inputs (22 m ha.)**

**Nutrient Depletion, specially SOC � One of the major biophysical constraints to food security and

economic development in the agriculture dependant rural areas (Ayoub, 1998).

This depletion, has exacerbated poverty which has contributed to greater environmental and land degradation.

Bad & Good ScenariosBad & Good Scenarios

�� BAD:BAD:

1.1. Recent report Recent report (IPCC)(IPCC) future scenario for Sudan and the Sahel future scenario for Sudan and the Sahel

region :region :

2.2. There will be increasing climatic stress on both agriculture There will be increasing climatic stress on both agriculture

and forestry, and forestry,

3.3. Climate is expected to get warmer in all seasonsClimate is expected to get warmer in all seasons

4.4. The semi arid areas, during the processes of land degradation The semi arid areas, during the processes of land degradation

and desertification, have lost great amounts of SOC. and desertification, have lost great amounts of SOC.

�� GOOD:GOOD:

1.1. A higher WUEA higher WUE in in AcaciaAcacia trees, in combination with the trees, in combination with the

ongoing increase in atmospheric COongoing increase in atmospheric CO22 concentration, concentration, can can

increase tree growth rates and act as a carbon sink. increase tree growth rates and act as a carbon sink.

2.2. A possible A possible a wina win--win situation can be reached win situation can be reached (increased SC (increased SC

storage +Improved productivity in degraded areas) storage +Improved productivity in degraded areas) (Ardo et (Ardo et

al. 2004)al. 2004)

Aim and scope of the study

The study was set out:

A. To contribute to better understanding of nutrients status and dynamics under A. senegal-based systems compared to pure cropping and grass fallow

B. To demonstrate an approach and methodology for

planning and assessment of nutrient flows and

balances in Acacia senegal-based systems, which

may be applied to other systems in the region or in

the country at large?

Specific Objectives

1. To assess flows and balances of OC, N & P at land management systems and cropping seasons levels.

2. To relate balances obtained to the baseline stocks to determine the degree of Depletion by the different land management systems.

3. To quantify and examine the impact of variations in input and output parameters on the resulting balances, to shed light on the relative sensitivity of the model (Optimistic & Pessimistic).

Materials and Methods

Methods and Approach

� The tool used is:

The nutrient balance model of a land use system (Stoorvogel and Smaling, 1990).

� The model determines net “surpluses” and “deficits”by measuring and summing all “imports” and “exports” of nutrients resources from a given plot

The term nutrient budget (balance)� (1) Quantifies the flows of nutrients within farming

systems

� (2) Estimates for a given plant nutrient the differences between inputs and nutrient outputs

Nutrient inflows and outflows

in farming systems

Conceptual Model used for analysis

of OC, N & P flows & balances

Inputs Outputs

Organic matter (IN1)

Atmospheric Deposition (IN2)

Biological N-fixation (IN3)

Harvested product (OUT1)

Plant residues (OUT2)

Leaching (OUT3)

Erosion (OUT4)

Balance = ∑ measured inputs –∑ measured outputs

Methods of Quantification of Flows

� Three methods were used to quantify nutrients flows:

(1) Primary data based on direct field

measurements and laboratory analysis,

(2) Estimates based on mixtures of :� field measurements,

� off-sites secondary data

� and/or transfer functions which are empirical relations

derived on the basis of aggregated knowledge from previous

studies, and

(3) Assumptions based on secondary data from

a variety of sources

Inputs data and method of quantification

Input Input & C& Codeode NutrientsNutrients Data requiredData required Method of Method of

QuantificationQuantification

11. Organic matter (IN2). Organic matter (IN2)

�� Tree Tree leaf leaf (IN2a)(IN2a)

�� RootRoot biomas in cbiomas in cropsrops

(IN2b),(IN2b), and grass (IN2c)and grass (IN2c)

OC, N, POC, N, P -- AmountAmount

-- ContentsContents

-- F. MeasurementsF. Measurements

-- L. analysisL. analysis

22.. Deposition (IN3)Deposition (IN3)

�� Dry (IN3a)Dry (IN3a)

�� Wet (IN3b)Wet (IN3b)

OC, N, POC, N, P -- % in DD% in DD

-- Average rainfallAverage rainfall

-- Rainfall recordsRainfall records

-- Transfer functionsTransfer functions

3. 3. N fixation (INN fixation (IN44))

��Symbiotic (IN4a)Symbiotic (IN4a)

��NonNon--symbiotic (IN4b)symbiotic (IN4b)

NN

-- % attributed to % attributed to

SNFSNF

-- % attributed to % attributed to

NSNFNSNF

-- Secondary data, Secondary data,

-- Transfer functionsTransfer functions

Outputs data and method of quantification

OutputsOutputs & Codes& Codes Data requiredData required Method of quantificationMethod of quantification

1. Harvested products 1. Harvested products

(OUT1)(OUT1)

1.1.Sorghum (OUT1a)Sorghum (OUT1a)

2. Roselle2. Roselle (OUT1b)(OUT1b)

3. 3. Gum Arabic (OUT1c)Gum Arabic (OUT1c)

-- Crop yield (sorghum, Crop yield (sorghum,

roselleroselle, gum), gum)

-- Nutrient contentNutrient contentss

-- Field measurementField measurement

-- lab.lab. Analysis & Analysis &

estimates.estimates.

2. Crop residues (OUT2)2. Crop residues (OUT2)

a. a. Sorghum (OUT2a)Sorghum (OUT2a)

b. Roselleb. Roselle (OUT2(OUT2bb))

c. Grassesc. Grasses(OUT2(OUT2cc))

-- AmountsAmounts

-- Nutrient contentsNutrient contents

-- Field measurementField measurement

-- Lab. AnalysisLab. Analysis

3. Leaching (OUT3)3. Leaching (OUT3) -- Average rainfallAverage rainfall

-- Leaching (N only)Leaching (N only)

-- Rainfall recordRainfall record

-- Estimate (Trans. Estimate (Trans.

Function).Function).

-- Secondary dataSecondary data

4. Erosion (OUT4)4. Erosion (OUT4) -- Average rainfallAverage rainfall

-- % nutrient content% nutrient content

-- Rainfall recordsRainfall records

-- Secondary dataSecondary data

Study area

North Kordofan States:

lies:

� Lat. 11°°°°: 15″ and 16°°°°: 30″N

� Long. 27°°°° to 32°°°°E

� within the arid and semiarid zones.

El Demokeya Forest:

� 35 km northeast of El Obeid, N K (southern zone).

� AAR 350 mm,

� Soils mainly sandy “Goz”.

� Area is about 3150 ha

Experimental Design and Treatments

1. Main plot factor: tree density � Level 1: 26 trees plot-1 (433 trees ha-1 (HD)

� Level 2: 16 trees plot-1 (226 trees ha-1) (LD)

� Level 3: No trees

2. Sub-plot factor: Crops� Level 1: sorghum (Sorghum bicolor) local variety “Zinari”

� Level 2: roselle (Hibiscus sabdarifa) local var. ”Rahad”

� Level 3: grass

Design:� RCBD

� 3 Reps.

� 27 plots 30 x 20 m (600 m2 each).

Treatments, their code and descriptions

Treatmen

t No.

Code Trees

ha-1Treatments description

1 HD+S 433 26 trees + Sorghum

2 LD+S 266 6 trees + Sorghum

3 PS No trees Pure Sorghum

4 HD+R 433 26 trees + Roselle

5 LD+R 266 16 trees + Roselle

6 PR No trees Pure Roselle

7 HِD+G 433 26 trees + Grass

8 LD+G 266 16 trees + Grass

9 PG No trees Pure Grass

Crop Management

Sowning: Spacing:

� Sorghum: 0.75 x 0.35 m.,� Roselle: 0.75 x 0.25m (ARC)

Sowing depth: about 0.3 m

Thinning: 2 plants/hole, two weeks from sowing.

Re-sowing:

� When necessary

Weeding: manually 3 times:

-1 week after sowing;

-at thinning, and

-at booting (sorghum) and flowering (roselle).

Crop protection: Zinc phosphate against rats

Field Measurements

� To get locally derived key values for different inputs &

outputs flows the following field measurements were

carried out:-

1. Tree biomass (litter & fine roots)

2. Gum Arabic yields,

3. Crop yields & biomass

4. Nutrient concentrations in plants components

5. Soil Nutrients stocks,

Acacia Senegal Biomass

� The study assumes that:

1. Nutrient contribution of Acacia senegal through OM is from

leaves litter and fine roots (

1. Diameters of all trees in each plot were measured at 0.3m height

2. The CSA (in mm2) were calculated for all trees in each plot.

3. For multi-stems trees all diameters were measured,summed up using Mac Dickens et al., (1991) formula:

D combined = √d2

1 +d22+d

23...d

2n. .................................... (1)

� Fine root biomass (YFR) in (g) was calculated as:

YFR= 23.4 x CSA0.512 r2 = 0.80 …................(2)

Litterfall from A. senegal

1. litter trays (each with 0.5 m2 catchments) were placed under the canopy of 3 trees (small, medium and large)

2. collected fortnightly, early June (flush time) to end of March (end of litter fall).

3. Fresh weight was determined

4. Samples were oven dried, weighed and analyzed for total OC, N and P.

� Total Litter/plot =

Average of 3 trees X trees per plot

� Total amounts /plot =

litter per plot X % nutrient contents in litter.

� Total litter/plot were extrapolated to kg ha-1 to calculate balances at land managements systems.

Aboveground Biomass of Crops and Grass

A. Inter-cropped plots:

� 4 quadrants constructed

1. 2 under tree canopy

2. 2 in the open (interspaces).

B. Pure crop plots:

� 5 quadrants constructed

1. 1 in the middle of the plot,

2. 4 at the four corners

� Sampling Time:

a. At harvest time(October) (for sorghum and roselle)

b. At peak of standing biomass (September) for grasses

Crops Belowground Biomass

� Assumptions:1. Below ground component (fine and coarse

roots) were considered as inputs to OM as roots turnover.

� Crops root biomass was estimated assuming root : shoot ratio of 0.1 (Jackson et al., 1996).

� Yr = Y x 0.1……………...............…… (3)

� Where:

� Yr is the root biomass (g).

� Y is total aboveground biomass (g) (straw plus grain yield).

Grass Belowground Biomass

� Grass root biomass was estimated using the formula:

1. RBO = 4.0 x BAG …………………………………….. (4)

2. RBU = 1.6 x BAG……………………………………... (5)

� Where:

� RBO is root biomass (g) in the open space,

� RBU is root biomass (g) under tree canopy, and

� BAG is total aboveground biomass (g).

� Root to shoot ratios (R/S) for grasses were:

1. 1.6 under canopy, and

2. 4.0 in the open (interspaces).

Soil Nutrients Stocks� Nutrient stocks under LMS was calculated for (0-0.3m) soil

depth.

� OC, N & P at differing canopy position for plots with trees was derived by summation (Deans et al., 1999):

(amounts in canopy zones + the open space)

� The soil nutrient stocks= soil volume X bulk density X nutrient concentrations

� expressed as kg/ha

Contents of OC, N & P in Plants OM (IN1)

A. Nitrogen & Phosphorus� Contributions of different plants components were obtained from:

1. Plant Biomass (Lab. Analysis)

2. Tree fine roots: ( estimates) based on values obtained by Deans et al.,

(1999) (1.70% for N, and 0.07 for P

B. OC in plants tissues:

� A generic concentration of (50%) was assumed and applied in

the analysis

1. Differences were insignificant in C contents among leaves, branches and

stem of the tree (Grunzweig et al. (2003)..

2. Other reports claimed that C contents of wood varies depending on the

species at least over a range from 47% to 59%.

3. A reassessment of C content in hardwood ( 46.3% to 49.9%), and in

softwood (47.2% to 55.2%) (Lamlom and Savidge, 2003).

Atmospheric Deposition (IN 3)

A. Dry Deposition (IN3a):� Dust can contribute up to: 3.0 kg N, & 1.0 kg P ha-1 yr-1

(Herrmann et al., 1996 and Ramisch, 1999).

� NO data for the study area, figures were applied in the calculations of (IN3a).

Wet Deposition (IN3b)

� NO available local data for the study area.

� The contribution of this pathways to N & P was calculated as the square root of the average annual rainfall using the regression equation derived by (Stoorvogel and Smaling (1990).

� The regression coefficients were:

� IN 3b (N) = 0.14 x √√√√RF…….………….……...... (6)� IN 3b(P2O5) = 0.053 x √√√√RF……………..……… (7)

� Where:

� IN3b= Nutrient content in kg ha-1 yr-1, and

� RF = rainfall in mm yr-1.

Rainfall (mm) during the experimental period (2002-2004)

0

20

40

60

80

100

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Months

Rain

fall

(mm

)

2002 2003 2004

2002=86 mm, 2003=324mm, 2004=196mm

Biological Nitrogen Fixation (IN4)

A. Symbiotc (IN4a)1. Data on biological nitrogen fixation by A. senegal is

controversial and contradicting

2. No empirical data as how much N is fixed by A. senegal

3. This make estimates very difficult.

� However, under the conditions of the studied site:

1. Semi-arid with rainfall ranging from 300-400 mm

2. Soils are sandy with very low mineral fertility

� It was assumed that SNF by A. senegal about 30% of the total N in the soil.

� B. Non-symbiotc (IN4b)

� In semi-arid ecosystems Non-symbiotic N

fixation can occurs through (Stoorvogel and Smaling, (1990), Felker et al., (1980)

� (1) Bacteria on roots & in degraded litter (1 kg

N ha-1 yr-1 )

� (2) Blue-green algae & lichens (2 kg N ha-1 yr-1)

� NO data for the study area, therefore, 3 kg

N ha-1yr-1 was used in the calculations of

(IN4b).

Crop Products ( OUT 1) & Residues (OUT 2)

A. Removal of nutrients in crop products comprises:

○ Sorghum grains (OUT 1a),

○ Roselle calyces and seeds (OUT 1b)

○ Gum Arabic (OUT 1c)

� The OUT1a, and OUT1b were calculated following formula developed by Stoorvogel and Smaling, (1990) :

OUT 1= Area (ha) x ∑∑∑∑ (nutrients contents x yield)________________________________………(8)

Total area of plot

B. Removal in crop residues comprise:

1. Sorghum stover (Out 2a),

2. Roselle stover (OUT 2b)

3. Grass aboveground biomass (OUT 2c)

quantified using the same formula as in (8)

Leaching (Out 3)

� NO Data available for the study site or for comparable agro-ecological zones nearby. Therefore we used:

� Regression equations by Stoorvogel and Smaling, (1990) .

� The equations derived using determinants of (rainfall, soil texture, clay content, soil N, inorganic (IN1), and organic(IN2) fertilizer.

(N) = 2.3 + (0.0021 + 0.0007 x F) x R + 0.3 x (IN 1 + IN2) - 0.1 x UN….. (9)

� Where:

○ · F = soil fertility class (1 - low; 2 - moderate; 3 - high),

○ · R = rainfall (annual average, mm);

○ · IN1 = inorganic fertilizers,

○ · IN2 = organic fertilizer;

○ · UN = total nitrogen uptake (kg ha-1 yr-1),

Leaching (Out 3)

� It was assumed that:

1. Leaching is only for N

� In tropical soils P is often tightly bound by soil particles (Stoorvogel and Smaling, 1990)

1. Soils of the study area have low fertility (F= 1),

2. IN1 and IN2 are zero

3. leaching occurs only in plots with pure crops.

� Evidence exists that :-1. Trees reduce nutrient leaching and form a safety net under the

root zone of annual crops (Van Noordwijk et al., 1996)

2. Trees are able to reduce nutrient leaching in comparison to pure

crops (Hartemink, et al., 1996)

Soil Erosion (OUT 4)

A. Nutrients in eroded soils:A. Nutrients in eroded soils:

�� Stoorvogel and Smaling (1990Stoorvogel and Smaling (1990)) estimatedestimated that:that:

�� TThe he % % nutrient contents transported in eroded soils of low nutrient contents transported in eroded soils of low fertility classfertility class (1)(1) land use systems in the order ofland use systems in the order of::

�� N= N= 0.05%,0.05%,

�� P= P= 0.02%0.02%

�� ForFor carboncarbon,, a value of 3% C a value of 3% C reported by reported by Sterk Sterk et al.,et al., (1996(1996)) was was

applied for loss due to wind erosionapplied for loss due to wind erosion. .

B. Amount of eroded soils:B. Amount of eroded soils:

�� In the study area In the study area ((Khair El Seid, (1997)Khair El Seid, (1997) estimated eroded estimated eroded soil through wind soil through wind as:as:

○○ 15.4 ton ha15.4 ton ha--11 yryr--11 on bare soilson bare soils

○○ 0.2 ton ha0.2 ton ha--11 yryr--11 on grass fallow on grass fallow

�� NONO available figures available figures forfor the study site, the the study site, the aboveabove values values were applied in the calculations of nutrient balance.were applied in the calculations of nutrient balance.

Sensitivity Analysis

1. This is warranted by the fact that a simple, accurate and

fully objective measure of nutrient flows is largely impossible

and modeling studies have to assume some “black boxes” or

otherwise the data collection task would be most infinite (Scoones and Toulmin, 1998).

2.2. SOMESOME values for values for OOC, N and P balances were estimates C, N and P balances were estimates based on field measurementsbased on field measurements & assumptions based on the & assumptions based on the secondary data secondary data are regarded as the are regarded as the ““most probablemost probable”” for the for the study area.study area.

3.3. SOMESOME inputs values:inputs values: atmospheric depositionatmospheric deposition,, nitrogen nitrogen fixationfixation, , leaching leaching && erosionerosion were estimates from secondary were estimates from secondary data within which there is some variability. data within which there is some variability.

4.4. Hence, to incorporate some of this uncertainty, sensitivity Hence, to incorporate some of this uncertainty, sensitivity analysis was conducted and analysis was conducted and three net nutrient balance values three net nutrient balance values werewere calculatedcalculated

Balance Scenarios

� (1) "Most probable",

� (2) " Optimistic", and

� (3) "Pessimistic".

�

� The “most probable” scenario is based on:

� direct measurements and secondary data typical

for the study area,

� It is assumed that it can best reflect the actual

nutrient balance of the study site.

Optimistic and Pessimistic Balances

� Using the values obtained in the "most probable" balances, the optimistic and pessimistic balances were calculated followed the procedures used by Krogh, (1997).

1. Optimistic balance:

A. High values of inputs

B. Low values of t outputs

� (1.25 x the values of inputs with 0.75 x values of outputs)

2. Pessimistic balance:

A. High values for outputs

B. Low values for inputs

� (0.75 x values of inputs with 1.25 x values of outputs).

Results

Average inputs , outputs and balances for OC (kg ha-1yr-1)

at LMS level

TRT Land management systems

HD+S LD+S PS HD+G LD+G PG HD+R LD+R PR

Total

inputs

3 106 b 2 184 d 187 g 3 342 a 2 503 c 483 f 3 230 ab 1 845

e

110 g

SE 87.1***

Total outputs 1 562 c 1 956 b 2 328 a 325 d 253 d 127 d 1 519 c 1 356

c

1 459 c

SE 145.5***

Total

Balance

1 544 c 228 e -2142 g 3 017 a 2 250 b 356 de 1 771 c 489 d -1 349 f

SE 155.7***

Positive OC balances in intercropped systems (overestimation of 50%) (0.34 % Manlay et al, 2002)

Average inputs , outputs and balances for N (kg ha-1yr-1)

at LMS level

TRT Land management systems

HD+S LD+S PS HD+G LD+

G

PG HD+R LD+R PR

Total inputs 312 a 215 c 13 e 298 b 222 c 10 e 325 a 186 d 10 e

SE 8.6***

Total

outputs

44 d 54 c 59 bc 6 e 5 e 3 e 70 a 62 b 54 c

SE 4.8***

Total

Balance

268 b 161 d -47 g 291 a 217 c 7 f 255 b 124 e -43 g

SE 9.5***

High estimate (10 to 20 kg N ha-1 years-1), mineralization, gains from sub-soil

(volatilization and gaseous losses)

Average inputs , outputs and balances for P (kg ha-1yr-1)

at LMS level

TRT Land management systemsLand management systems

HD+S LD+S PS HD+G LD+G PG HD+R LD+R PR

Total inputs 11 b 8 d 2 g 12 a 10 c 4 f 11b 7 e 2 g

SE 0.3***

Total outputs 10 e 13 de 15 d 1 f 1 f 1 f 35 a 31 b 25 c

SE 2.5***

Total Balance 1 b -5 c -13 d 11 a 9 a 3 b -24 e -24 e -24 e

SE 2.5***

Cropping seasons Inputs & Outputs (kg ha-1yr-1)

Average balances for P (kg ha-1yr-1) at cropping seasons

cropping

seasons

OC N P

2002 1559 a 166 a 5.0 a

2003 -140 c 109 c -20 c

2004 617 b 134 b -10 b

Mean 678 137 -8

SE 89.9*** 5.5*** 1.4***

Nutrient depletion

(stocks relative to average balance)

LMS Stocks (kg ha-1) June,

2002**

Balances (kg ha-1) in

2004

% depletion of stocks

OC N P OC N P OC N P

HD +S 4029 921 11 1544 268 1 38 29 9

LD +S 4633 855 12 228 161 -5 5 19 -42

PS 4387 548 10 -2142 -47 -13 -49 -9 -130

HD +R 4342 1035 11 1711 255 -24 39 25 -218

LD +R 5101 880 12 489 124 -24 10 14 -200

PR 4677 668 11 -1349 -43 -23 -29 -6 -209

HD +G 3862 893 11 3017 291 11 78 33 100

LD +G 4732 881 12 2250 217 9 48 25 75

PG 5396 700 12 356 7 3 7 1 25

Means “Optimistic” and ”Pessimistic” (kg ha-1 yr-1)

for OC, N & P at LMS levels

LMS Optimistic balances • Pessimistic balances

C N P C N PHD +S 2711c 358a 6.0c 377 cd 181b -5.0c

LD +S 1263d 229c 0.0d -807f 95d -10d

PS -1513g -28f -9.0e -2770h -64g -17e

HD +R 2899bc 351a -15f 525c 155c -40g

LD +R 1289d 184d -17fg -312e 61e -38fg

PR -956f -28f -19g -1740g -59g -34f

HD +G 3934a 366a 14a 2100a 213a 7a

LD +G 2939b 273b 11b 1561b 159c 6ab

PG 508d 10e 4.0c 204d 4.0f 2.0b

SE 139.8*** 11.1*** 1.9*** 181.9*** 8.5*** 3.1***

Means “Optimistic” and ”Pessimistic” (kg ha-1 yr-1) for

OC, N & P at cropping seasons levels

Cropping

Seasons

Optimistic balances Pessimistic balances

OC N P OC N P

2002 2055 a 211 a 7.0 a 1063 a 121 a 3.0 a

2003 895c 171c -11c -1176 c 47 c -29c

2004 1408b 189b -4b -174b 80b -16b

Mean 1238 193.0 -3.0 -288 88.0 -14

SE 80.7*** 6.4*** 1.10*** 105.1*** 4.9*** 1.8***

Conclusions

&

Recommendations

� The study showed that flows & balances of

studied nutrients were significantly different

among and within LMS.

� Because:

1. LMS were in general biologically and

agronomically diverse.

1. All LMS are low-input farming systems (no

application of external inputs).

2. Overall, the majority of inputs and outputs

pathways in different LMS are: tied to tree

biomass, crop residues and grass, or a

combination of them.

�� Gum yield Gum yield had insignificant contribution to had insignificant contribution to both N and P inflows and outflows in treeboth N and P inflows and outflows in tree--based based systems. systems.

�� Crops roots and atmospheric depositions Crops roots and atmospheric depositions (wet (wet & dry) had insignificant contribution to & dry) had insignificant contribution to nutrients inflowsnutrients inflows

� The results raise some concerns about land use

sustainability of the sandy soils in North

Kordofan.

�� The results showed that:The results showed that:

A. A. pure cropping hadpure cropping had high negative balances of high negative balances of

OC, N & POC, N & P, for all balance scenarios , for all balance scenarios (average, (average,

optimistic optimistic && pessimistic)pessimistic)..

� Given the low soil fertility of the site, this

imply that this LMS is not sustainable, not

only at high productivity, but even at low

productivity, because the levels of imports

were less to compensate nutrients exports.

B. In intercropped systems:

� land use sustainability is under threats from

high negative P balances at low productivity

“optimistic”

� Phosphorous availability is of significant concern

for all intercropped roselle systems.

� At high productivity “pessimistic” balances.

phosphorous is of much great concerns in

sorghum and roselle at LD & HD.

� If this situation sustained, P would be depleted

in the long run, posing a threat to long-term

productivity of the land.

� In spite of its diagnostic nature, the study demonstrated that nutrient balance model serves to identify the important nutrients flows and pathways under the different LMS in the study site.

� Hence, it can be used:

� As indicator of sustainability

� As a tool in nutrient management within farmers fields

Recommendations

1. The balances showed that tree components is a major contributor of SOM to farming systems.

� Therefore: Whole-tree clearance:

� Not only deprive SOM which supply most important nutrients (N, P and S),

� but also leave it prone to wind and water erosion with consequent decline in productivity.

2. Hence, means to adjust these balances are

needed to improve land use sustainability

such as:

� Return and incorporation of crop residues,

� Land tillage,

� Application of animal and green manure,

� Planting of leguminous crops, and

� Sensible use of micro-fertilization.

Thank you very muchThank you very much