Estimation of Effective Fallow Availability for the Prediction of Yam Productivity

Home , Yam (vegetable)

Field Crops Research 131 (2012) 32–39

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Field Crops Research

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Estimation of effective fallow availability for the prediction of yam productivity

at the regional scale using model-based multiple scenario analysis

a,∗ a,1 b,2 c,3

Amit Kumar Srivastava , Thomas Gaiser , Denis Cornet , Frank Ewert

University of Bonn, Institute of Crop Science and Resource Conservation, D-53115, Bonn, Germany

INRA Gouadeloupe, 97170 Petit Bourg, Guadeloupe

University of Bonn, Institute of Crop Science, Katzenburgweg 5, 53115 Bonn, Germany

a r t i c l e i n f o a b s t r a c t

Article history: Soil fertility restoration and crop performance in many developing countries with low input agriculture

Received 12 September 2011

strongly relies on fallow duration and management. More precise information about the availability of

Received in revised form 17 January 2012

fallow land may provide a way to improve the simulation of yam (Dioscorea spp.) yields at the regional

Accepted 17 January 2012

scale which has hardly been considered in prevailing approaches to model regional crop production. The

probable reason behind this is scarce availability of data on fallow duration and variation across the farms

Keywords:

in a region. Therefore, this study attempts to estimate effective fallow availability for yam production

Fallow availability

at the regional scale and to simulate the effect of fallow on regional yam yield. Yam growth and yield

Yam

were simulated with the EPIC model which was incorporated into a Spatial Decision Support System

Crop growth model

Up-scaling (SDSS) covering a typical catchment with variable land use intensity within the sub-humid savannah

West Africa zone of West Africa. Yam-fallow rotations were simulated within 1120 quasi-homogenous spatial units

(LUSAC = Land Use-Soil Association-Climate units) and aggregated to the 121 sub-basins and ten districts

within the catchment under three different scenarios of fallow availability: (S1) Total savannah area was

available as fallow land, (S2) 50% of the bush savannah was available as fallow land and (S3) 25% of the

bush savannah was available as fallow land. The aggregation procedure adopted in this study was based on

changes in the frequency of fallow-cropland classes within the sub-basins to render the SDSS sensitive

to changes in fallow availability. Comparison of the average simulated tuber yield with the observed

mean yield over the entire catchment showed that the simulations slightly overestimated the yields by

0.4% for scenario S1, whereas, underestimated by 14.2% and 36.8% in scenario S2 and S3 respectively.

When compared with the effective fallow availability to maize, it was concluded that, (1) due to farmers

preferences to plant yam mainly on virgin savanna land and as the ﬁrst crop in the rotation after fallow,

the effectively available fallow area for yam is higher than for maize and (2) the applied approach is

suitable to derive effective fallow availability for yam production at the district scale.

1. Introduction twenty years mostly for the supply of urban markets (Scott et al.,

2000). Yam cultivation techniques use very few chemical inputs,

After cassava (Manihot esculenta Crantz.) and sweet potato (Ipo- the labor is essentially manual and the cultivated varieties are

moea batatas L. Lam.), yam (Dioscorea spp.) is the third most mainly landraces (Scott et al., 2000). In most production systems,

important tropical root crop in West Africa, Central America, the yam is grown almost without external inputs following long term

Caribbean, Paciﬁc Islands and Southeast Asia (Onyeka et al., 2006). fallow (Degras, 1993). This means that yam production depends

During the past 30 years, yam production increased threefold with on the availability of fallow land and expansion of yam production

an annual growth rate of 3.8%. A rate of 3% is expected for the next requires that more and more savanna land is cleared for cultiva-

tion. Slash and burn techniques remain the common practice for

land clearing but are used less extensively due to rapid popula-

tion growth, high pressure of cattle and changes in land tenure.

∗

Corresponding author. Tel.: +39 0332 783889; fax: +39 0332 783033. In the transitional forest-savannah agro-ecological zones of West

E-mail addresses: amit [email protected], [email protected]

Africa, fallow periods have decreased from 20 to 30 years in tra-

(A.K. Srivastava).

1 ditional farming to less than 5 years (Ouédraogo, 2004; Aihou,

Tel.: +49 228 732050; fax: +49 228 732870.

2 2003). Therefore, yam yields are reported to decline sharply when

Tel.: +33 590255975; fax: +590 590941663.

Tel.: +49 228 732871; fax: +49 228 732870. grown after short fallow of about 1–3 years duration (Watson

A.K. Srivastava et al. / Field Crops Research 131 (2012) 32–39 33

Table 1

and Goldsworthy, 1964). Hence, fallow availability and its dura-

Comparison of observed and simulated mean tuber yield of yam, mean residual

tion is the main driver for yam production in West Africa (Zannou

error (ME), and mean absolute error (MR) after model calibration at the ﬁeld scale

et al., 2004; Sullivan, 2010). Fallow duration and the ratio between

(Srivastava and Gaiser, 2010).

cropping and fallow period strongly inﬂuence soil properties, crop

−1 −1

Treatment Simulated (Mg ha ) Observed (Mg ha ) ME MR (%)

yields and cropping sustainability (Szott et al., 1999; Samake et al.,

−

2005; Diekmann et al., 2007). At the farm or regional scale, the Control 2.44 2.59 0.15 5.7

Fertilized 3.72 4.12 0.40 −9.7

relationship between fallow and cropland is reﬂected by the fallow-

Optimum 3.94 4.12 0.18 −4.3 cropland-ratio (Ruthenberg, 1980).

fertilization

Many regional assessments of crop production have been car-

ried out in developed countries or with crops which are usually

grown at moderate to high input intensities where the effect of fal- 2.2. Crop growth model

low on yield performance is negligible (Thomson et al., 2005, 2006;

Liu et al., 2007; Hartkamp et al., 2004; Soler et al., 2007; Challinor A single plant growth model is used in EPIC to simulate biomass

and Wheeler, 2008; Gaiser et al., 2008). Only few attempts have accumulation and crop yield of about 130 crops, each with a unique

been made to incorporate fallow effects into crop modeling at the set of growth parameters (e.g., radiation use efﬁciency, RUE; poten-

catchment or regional scale (Van Noordwijk, 2002). However, in tial harvest index, HI; optimal and minimum temperatures for

order to estimate the effect of fallowing at larger spatial scales, growth; maximum leaf area index, LAI; and stomatal resistance).

there is a need of new methods to quantify the ratio between the EPIC is capable of simulating growth for both annual and peren-

cropland area in a given geographic region and the fallow land area nial crops. Annual crops grow from planting date to harvest date or

which is available for the farmers to be integrated into their fallow- until the accumulated heat units during the simulation equal the

crop rotation (Gaiser et al., 2010b). Quantiﬁcation of the cropland potential heat units for the crop (Williams, 1995). EPIC estimates

area in a given region is relatively easy when agricultural statis- crop yields by multiplying aboveground biomass at maturity by

tics are available. In addition, statistical information about cropland a harvest index. For non-stressed conditions, the harvest index is

area can also be cross-checked with the results of well established affected only by the heat unit index. The ﬁnal HI is estimated based

land use classiﬁcation approaches from remote sensing data. The on the potential HI, minimum harvest index, and water use ratio.

extent of un-cropped land which is potentially available as fallow The model estimates potential biomass based on the interception

land can be derived in the same way from remote sensing informa- of solar radiation and the RUE that is affected by vapor pressure

tion. However, the effective availability and usage of fallow land deﬁcit and by atmospheric carbon dioxide concentration. Water,

by the farmer within the fallow-crop rotation is much lower than nutrient, temperature, aeration, and radiation stresses restrict daily

the potential or apparent fallow area due to several factors like accumulation of biomass, root growth, and yield (Williams, 1995).

accessibility to the land (which is related to infrastructure facil- Stress factors are calculated daily and range from 0.0 to 1.0. For

ities), distance of the fallow land from the homesteads and land plant biomass, the stress used on a given day is the minimum of

rights (Brown, 2006). Given the importance of available fallow land the water, nutrient, temperature, and aeration stresses. For root

for yam production in low-input fallow systems in Benin Repub- growth, it is the minimum of the calculated soil strength, tempera-

lic, methods for estimating the effective fallow availability at the ture, and aluminum toxicity stresses. In addition, yield reductions

regional scale need to be improved. Therefore, the objective of this of grain crops are calculated through water stress-induced reduc-

paper is to estimate effective fallow availability for yam production tions of the HI. With the EPIC crop model, sensitivity of yam yield

at the regional scale by exploring different fallow scenarios and to to fallow duration relies mainly on mineral nutrition and organic

compare the results with the effective fallow availability for maize matter management.

as the dominant food crop. A prerequisite to use a ﬁeld scale model at the regional scale

lies in the evaluation of the model performance at different areas

in the target region. The EPIC model version 3060 was earlier

2. Material and methods

calibrated for yam growth and yield at ﬁeld scale in the Upper

Oueme catchment of Benin Republic (Srivastava and Gaiser, 2010).

The procedure to estimate the effective fallow availability is

This calibration referred to fertilized and unfertilized ﬁeld trials

illustrated in Fig. 1. Spatial information about the location of climate

during 2005–2006. In 2005 and 2006, the mean simulated yields

stations, soils, cropland and potential fallow land are combined

agreed well with the observed means resulting in model errors

with assumptions regarding fallow availability into spatial simula-

ranging from 5.7 to 9.7% (Table 1). The model has been applied

tion units (LUSAC). The outcome of the scenario simulations at the

for yam (Srivastava and Gaiser, 2010), cassava, millets, sorghum

LUSAC level is aggregated to the district level and then compared to

(Adejuwon, 2004) and maize with reasonable accuracy except for

the observed yam yields in order to estimate effective fallow avail-

sites with highly acid soils (Gaiser et al., 2010a). In the present

ability for yam production in each district. The components and the

study, EPIC version 3060 has been used at the regional scale within

procedure are explained in this section.

the same catchment where calibration has been carried out (see

Section 2.3).

2.1. Model description

2.3. Study area and simulation units

The EPIC model consists of nine integrated sub-models: hydrol-

ogy, weather, erosion, carbon and nutrient cycling (N, P, and K), 2

The Upper Oueme catchment covers an area of 14,500 km

plant growth, soil temperature, tillage, economics, and plant envi-

within the Republic of Bénin. The climate is tropical sub-humid

◦

ronment control (Williams, 1995). In its current form, EPIC is well

with mean annual temperature of 26.8 C and mean annual precip-

suited for assessing the effects of soil erosion on crop productiv-

itation of 1150 mm (Mulindabigwi et al., 2008). According to the

ity, predicting the effects of management decisions on soil, water,

FAO soil classiﬁcation (FAO, 2006), the predominant soils are Luvi-

nutrient, and pesticide movements, and tracing the allocation and

sols with variable depth and coarse fragment content. Soils with

turnover of C and N in soil. The model operates on a daily time step

plinthic layers occur frequently (Giertz and Hiepe, 2008). Soil tex-

and is capable of long-term simulations of up to 4000 years with

ture in most of the cases is sandy in the top layers and loamy to

soil proﬁles having up to 10 layers.

clayey in the subsoil. Soil pH is neutral to slightly acid.

34 A.K. Srivastava et al. / Field Crops Research 131 (2012) 32–39

Fig. 1. Flow chart illustrating the procedure to estimate effective fallow availability for yam production.

6000

official statistics 34 33 5000 own observations ) -1 109 4000

3000 89

2000 Yam dry matter (kg ha 1000

0 2001 2003 2003 2002/03

Tchaourou Sinende Glazoue Tchaourou

Fig. 2. Comparison of yield data provided by the ofﬁcial statistics with own observations from farm surveys and ﬁeld experiments (number above bars is the number of own

observations).

All data which was necessary to delineate the spatial simula- and combining them with different fallow-crop rotations (Table 2).

tion units (Fig. 2) were gathered in the database of the spatial The fallow-crop rotations were distributed randomly on the crop-

decision support system SDSS PEDRO (Protection du sol Et Dura- land identiﬁed from satellite images by Judex (2008) depending

bilité des Ressources agricoles dans le bassin versant de l’Ouémé; on scenario assumptions. The resulting LUSAC units (LUSAC = Land

Enders et al., 2010), which combines the agro-ecosystem model Use-Soil Association-Climate units) had variable surface area and

EPIC with the hydrological model SWAT (Arnold et al., 1998). represented spatial units with similar climate conditions, soil char-

PEDRO provided representative soil proﬁle data for the dominant acteristics and fallow-crop rotations. Thus, each sub-basin was

soil type of each of the 38 mapping units of the soil association ﬁnally composed of up to 15 LUSAC units of variable size which

map. Topographical information including average slope inclina- constituted the spatial simulation units.

tion and length were extracted from the DEM (Digital Elevation

Model) provided by the global SRTM (Satellite Radar Topographic

2.4. Simulation of tuber yield at LUSAC scale and up-scaling

Mission) (USGS, 2005). Based on the topographical information, method

the catchment was subdivided into 121 sub-basins. Then, a total

of 1120 simulation units were identiﬁed by overlaying information

The simulation of tuber yield and up-scaling of the simulation

of the location of the climate stations with the soil distribution map

results to obtain regional estimations of mean tuber yield for each

Table 2

Deﬁnition of regional fallow–crop classes according to their average fallow–cropland ratio.

Fallow–cropland class Cropland area (%) Fallow–cropland ratio Crop cycles without fallow Fallow–crop cycles

Area (%) Cropland (%) Area (%) Cropland (%)

1 17 4.9 0 100 100 17

2 25 3.0 10 100 90 17

3 38 1.6 25 100 75 17

450 1.0 40 100 60 17

5 67 0.5 60 100 40 17

683 0.2 80 100 20 17

7 100 0.0 100 100 0 17

A.K. Srivastava et al. / Field Crops Research 131 (2012) 32–39 35

Table 3

Proportion of land use classes and resulting fallow–cropland ratio within the Upper

Oueme catchment.

All sub-basins Sub-basins with cropland

2 2

Km % of total area Km % of total area

Cropland 1745 12.1 1745 19.8

Bush savanna 8403 58.2 5324 60.4

Tree savanna 4284 29.7 1752 19.9

Total 14432 100.0 8821 100.0

Average fallow–cropland 7.3 4.1

ratio

sub-basin was done according to the procedure described by Gaiser

et al. (2008). The procedure consists in the following steps (Fig. 2).

a. Preparation of EPIC input database ﬁle for each LUSAC unit.

Fig. 3. Workﬂow for the up-scaling of EPIC simulation results to the sub-basin and

b. Running the simulations for each LUSAC unit.

district level.

c. Extracting the output ﬁles and transfer it to the database.

d. Aggregation of the results at district level.

Each sub-basin was linked to the nearest climate station. A

In the next step, each of the 64 sub-basins were classiﬁed accord-

total of 14 rain gauging stations were available with daily rainfall

ing to their average fallow-cropland ratio in the three scenarios.

measurements from 1960 to 2005. Daily minimum and maxi-

Seven fallow-cropland classes were deﬁned with average percent-

mum temperature for these 14 stations were generated by the

age of cropland between 17% (Fallow-cropland class 1) to 100%

REMO model which has been previously calibrated for the period

(Fallow-cropland class 7). In fallow-cropland class 1 the average

1960–2000 (Paeth and Thamm, 2007). For the calculation of the

fallow-cropland ratio is 4.9 (Table 2) which means that 4.9 ha of fal-

potential ET the approach given by Hargreaves and Samani (1985)

low are available against 1 ha of cropland, whereas in fallow-crop

was used. Cropping systems in the Upper Oueme catchment are

class 7 no fallow land is available and all land is used for cropping.

characterized by low-input agriculture. In case of yam no fertilizer

Thus, from class 1 to 7 the fallow-cropland ratio and hence fallow

is applied and soil fertility restoration depends exclusively on the

availability are decreasing. Each sub-basin was categorized into one

duration of the fallow period. Hence, the availability of fallow area,

of these seven classes.

which is reﬂecting the duration of the fallow period is crucial for

Crop management was deﬁned in the simulations according

crop production.

to the prevailing, traditional ﬁeld activities for long cycle vari-

eties of white yam (Dioscorea alata), which are dominant in the

2.5. Availability of fallow area and fallow distribution procedure

study region. The start and end of the growing season was set to

February and December, respectively. Plant density was set at 0.88

In order to assess the effective fallow availability per district, the −2 ◦

plants m . Potential heat units (PHU) were estimated as 1945 C

approach proposed by Gaiser et al. (2010b) was applied. In the ﬁrst

using the average of 2-year growing degree days (GDD) accumu-

step, the total cropland in the year 2000 as well as the non-cropped

lation during the normal growing season for D. alata (Srivastava

area excluding settlements were estimated for each of the 121

and Gaiser, 2010). Switch grass (Panicum virgatum) was used as

sub-basins from satellite images (Judex, 2008). The cropland had

the fallow crop in the simulations. The start and end period of the

a proportion of 1745 km or 12.1% (Table 3). The non-cropped area

growing season of switch grass was set to March and February,

was classiﬁed into “Bush savannah” and “Tree savannah (including

respectively. Replanting was done every year to mimic the process

forest)” with a total proportion of 58.2% and 29.7%, respectively. −2

of re-growth after the dry season. Plant density was 10.0 plants m

The major part of the land use class “Tree savannah” belongs to ◦

with PHU of 5000 C. No mineral fertilizer was applied in the

protected forest areas, where cropping is prohibited. Therefore, in simulations.

a second step, the sub-basins which are located entirely or with a

major proportion within these protected areas were excluded from

the study, as well as sub-basins where the proportion of cropland

2.6. Data aggregation and statistical analysis

was lower than 5%. Then, for the remaining 64 sub-basins, different

scenarios of fallow availability were deﬁned:

For the regional analysis, yield data were available at the dis-

trict (French: commune) level in Benin (INRAB, 2005). Data series

Scenario 1: Total savannah area is available as fallow land. from 1999 to 2004 were available from the National Agricultural

Scenario 2: 50% of the bush savannah is available as fallow. Research Organisation (INRAB). The yield information at the district

Scenario 3: 25% of the bush savannah is available as fallow. level provided by INRAB statistics is an average yield from randomly

selected farmer ﬁelds, where crops are harvested from 400 m

In the remaining sub-basins the percentage of cropland was on plots. Fig. 3 shows that the statistical information ﬁts quite well our

average 19.8% (Table 3), which corresponds to a fallow-crop ratio own observations from three selected districts. The observations in

of 4:1 or a fallow-crop-cycle with 1 years cropping and 4 years of the district of Tchaourou were from predominantly degraded sites

fallow. However, in some sub-basins the proportion of cropland and were therefore slightly lower than those provided by INRAB. As

was lower or higher. Therefore, for the simulations at the LUSAC observations were only available for the period 1999–2004, simula-

level, fallow-crop cycles with 1 year of cropping and 5 years of tion of tuber yield of yam was carried out within each LUSAC for the

fallowing and a crop rotation without fallow were also deﬁned. same period. Then, tuber yields were aggregated from the LUSAC

Simulation duration was 17 years from 1988 to 2004. level to both the sub-basin and commune level taking into account

36 A.K. Srivastava et al. / Field Crops Research 131 (2012) 32–39

the area share of the LUSACs within these larger spatial units in

order to obtain the average tuber yield Ys as N

Ys = Yi × Areai

with Ys is the average tuber yield per sub-basin/district in

−1 −1 −1 −1

t ha a ; Yi is the tuber yield in LUSAC unit i in t ha a .

Areai is the decimal area percentage of LUSAC unit i in the

respective sub-basin/district. As a measure of precision to compare

statistical data and simulated values the following parameters were

used:

a. The mean residual error ME as

ME = 1

(y − x ) n i i

i=1

b. The mean absolute error MR as

n y 1 i − xi

MR = n xi

i=1

c. Root Mean Square Error RMSE as n 0.5

1 2 100

RMSE = (yi − xi) n x¯

i=1

Where n is the sample number, x is the observed and y is the sim-

ulated value. A value of 0 of mean residual error (ME) indicates no

systematic bias between simulated and measured values. The mean

relative error (MR) gives an indication of the mean magnitude of the

error in relation to the observed value. Small values indicate little

difference between simulated and measured values.

The minimum value of Root Mean Square Error (RMSE) which

is 0 indicates that simulated and measured values are identical.

Regression analysis was performed with the MS EXCEL software

program.

3. Results and discussion

3.1. Distribution of fallow classes and model validation

The frequency distribution of fallow-cropland classes obtained

by the fallow distribution procedure is sensitive to the scenarios

of fallow availability. In Scenario 1, where all non-cropped land is

available for fallowing, 38 out of 64 sub-basins belong to the fallow-

cropland class 2 with an average fallow-cropland ratio of 3.0, which

means that 3 ha of fallow are available for one hectare of cropland

(Fig. 4). On the other hand, in scenario 3, where it is assumed that

only 25% of the bush savannah is available as fallow land, none of

the sub-basins falls into this class, but 50% of the sub-basins fall

into class 4 with a fallow-cropland ratio of 1.0.

In the period 1999–2004, the mean annual tuber yield observed

−1

in the 10 districts of the Upper Oueme catchment was 4.51 Mg ha

according to the reports of the agricultural statistics (Table 4).

Fig. 4. Spatial distribution of fallow-cropland classes in the Upper Oueme catchment

Fig. 5 shows that the correlation between the observed and as affected by three different scenarios of fallow availability (left: 100% availability

of fallow land; right: 25% availability of fallow land; down: 50% availability of fallow

simulated mean yam tuber yields for the 10 districts, with coef-

land). For description of fallow-cropland classes see text and Table 2.

ﬁcients of determination between 0.27 and 0.15 depending on

the assumptions with respect to fallow availability. When the

entire non-cropped area would be considered to be available as

fallow land (Scenario 1), the mean yield over the 10 districts,

−1

i.e. 4.51 Mg ha (INRAB, 2005), is overestimated by 0.44%. Gaiser

A.K. Srivastava et al. / Field Crops Research 131 (2012) 32–39 37

Table 4

Mean observed tuber yield, simulated tuber yields and simulated mineralized nitrogen content over 10 districts within the Upper Oueme catchment from 1999 to 2004 for

three scenarios with different fallow availability (ME = mean error, MRE = mean relative error and RMSE = root mean square error).

−1

Scenario Yam area with crop fallow Simulated annual Observed yield Simulated yield ME (Mg ha ) MRE (%) RMSE (%)

−1 −1

ratio >4 or yam area after 4 mineralized (Mg ha ) (Mg ha )

−1

years of fallow. (%) nitrogen (kg ha )

S1: All rangeland as fallow 83.8 191.4 4.51 4.53 +0.02 +0.4 28.3

S2: 50% rangeland as fallow 71.1 164.1 4.51 3.87 −0.64 −14.2 25.6

S3: 25% rangeland as fallow 52.8 118.9 4.51 2.85 −1.66 −36.8 38.0

Scenario 1 Scenario 2 Scenario 3 6 6 6 ) )

) y = 0.60x + 1.20 y = 0.54x + 0.47

-1 y = 0.47 + 2.49 -1 -1 2 2 5 R2 = 0.15 5 R = 0.27 5 R = 0.18

4 4 4

3 3 3

2 2 2 Simulated yield (Mg ha Simulated yield (Mg ha Simlulated yield (Mg ha 1 1 1 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 -1 Observed tuber yield (Mg ha ) Observed tuber yield (Mg ha-1) Observed tuber yield (Mg ha-1)

−1

Fig. 5. Simulated versus observed average yam tuber yields (in Mg ha ) of 10 districts belonging to the Upper Oueme catchment for three different scenarios with decreasing

fallow availability.

Table 5

Mean observed tuber yield, yield difference, fallow-cropland ratio and proportion of the protected area in the 10 districts of the Upper Oueme catchment.

Mean yield (1999–2004) Yield difference (simulated – Fallow–cropland Proportion of

−1 * −1

(Mg ha ) observed) (Mg ha ) ratio protected area (%)

Pehouco 4.5 1.26 4.7 0.0

Tchaourou 4.7 0.51 3.7 46.2

Kouandé 4.2 0.44 1.6 0.0

N’Dali 4.4 0.05 4.4 19.6

Parakou 3.7 0.03 3.0 0.0

Bembèrèkè 4.0 −0.17 3.8 0.0

Bassila 4.7 −0.22 4.1 6.2

Kopargo 4.9 −0.24 1.6 0.5

Djougou 5.1 −0.28 3.2 8.6

Sinendé 5.3 −0.96 4.6 0.0

Scenario 1: 100% fallow availability.

et al. (2010a) reported from a multi-location validation in tropi- fallow based agricultural systems (Fig. 6). When available fallow

cal regions, that maize yield predictions by the EPIC model at the land is reduced to 50 or 25% (which corresponds to mean fal-

ﬁeld scale are overestimated on highly acid soils. However, this is low durations of less than 4 years) the mean annual yam yield

−1

not the case in Central Benin, where mean soil pH ranges between decreases from 4.53 to 3.87 and 2.85 Mg ha , respectively, over

6.1 and 7.4 in the topsoil and 6.1–7.9 in the subsoil (Junge, 2004; all districts (Table 4). This is in line with the ﬁndings of Watson

Igue, 2000). On the other hand, a major factor determining yields and Goldsworthy (1964), who reported that yam yields decline

at the farm and regional scale in West Africa is the availability of sharply when grown after short fallow of about 1–3 years duration.

fallow and to which extent it is included in the crop rotations. With For the scenarios 2 and 3 the mean errors between simulated and

decreasing availability of fallow land, the mean simulated crop

yield at the district level decreases which is explained by the sim-

120

ulated changes in mineralized nitrogen (Table 5). With decreasing

fallow availability in the simulations, there is a decrease in nitrogen

) 100

mineralization (Table 5). Yam is often the ﬁrst crop grown after fal- -1

low. According to the literature, the integration of yam production 80

with slash-and-burn practices is the consequence of the high soil

fertility requirement of yam (Orkwor et al., 1998). Kabeerathumma 60

Yam- fallow rotaon

et al. (1991) showed that total exportation of N, P and K, respec-

No Fallow

40 tively, range between 73 and 205, 21 and 57, and 77 and 260 kg (permanent yam

cropping)

of nutrients per hectare depending on species and varieties. How-

Organic carbon (Mgha 20

ever, little information is available on nutrient requirements of

yam for optimum yield and how to satisfy them. The simulated

mean soil organic carbon content over all cropland during the sim- 1988 1990 1992 1994 1996 1998 2000 2002 2004

Years

ulation period was also sensitive to the inclusion of fallow into

the yam rotation. Permanent yam cropping showed a decline in

Fig. 6. Comparison of soil organic carbon content under permanent yam cropping

soil organic matter which is the prime source of nutrients in the

(i.e., no inclusion of fallow crop) and yam-fallow rotation.

38 A.K. Srivastava et al. / Field Crops Research 131 (2012) 32–39

−1 −1 −1 −1 6000

observed yields are −0.64 Mg ha a and −1.66 Mg ha a , with

− y = 57.3x + 4366 mean relative errors of 14.2% and −36.8%, respectively. Therefore,

R2 = 0.02

the best agreement between simulated and observed yields has 5000

been realized under the Scenario 1 (i.e., 100% of the bush savannah )

-1

is available as fallow) which suggests that effective fallow avail-

4000

ability over the entire catchment is 100% and that 83.8% of the land

area, fallowed for at least 4 years, is available for yam cultivation

(Table 4). This is in contrast to the effective fallow availability for 3000

maize, which is below 25% in the Upper Oueme catchment of Cen-

tral Benin corresponding to an effective fallow-cropland ratio of less Yam yield (kg ha

2000

than 1 (Gaiser et al., 2010b). It has been frequently reported that

yam has a high priority in the cropping systems in the cereal-root a

zone of West Africa (Forde, 1937) and elsewhere (Sullivan, 2010). 1000

In the humid savanna, yam is planted in rotation with maize and 0.0 1.0 2.0 3.0 4.0 5.0

cassava whereas in the less humid savanna, sorghum and maize are

Fallow-cropland ratio

the main crops in the rotation (Diehl, 1992; Forde, 1937; Richards,

1983). However, in all cases yam is used as the ﬁrst crop in the rota- 1800

y = 118.81x 2 - 621.77x + 1833.8

tion after fallow. Yam farmers purposely are searching for virgin

R2 = 0.68

savanna land, preferably with sparse trees, where the tree trunk can

1600

serve as stake for the yam vine after clearing and burning. Hence,

yam is the pioneer of deforestation in the humid savanna and yam

)

1400

farmers are moving either temporarily or permanently to explore -1

fertile savanna land. Singer (2005) reports about the tremendous

increase of new village in the district of Tchaourou (Central Benin)

1200

within the last 30 years of the last century and a proportion of exter-

nal labor forces from other districts of more than 25%. A case of a

single farmer family is described which moved from its homestead 1000

Maize yield (kg ha

at least four times within 55 years (1935–1990), “hunting for fertile

land”. Thus, as long as there are reserves of virgin savanna land in

800

a given region, the farmers will move and explore it, regardless of

the average fallow-cropland ratio in this region. This may be the b

reason why, between 1998 and 2004, observed yam yields at dis- 600

0.0 1.0 2.0 3.0 4.0 5.0

trict level were independent of the fallow-cropland ratio (Fig. 7).

Comparison with the relationship between fallow-cropland ratio Fallow-cropland ratio FU100

and maize yields for the same 10 districts demonstrates the par-

Fig. 7. Relationship between fallow-cropland ratio in 10 districts of the Upper

ticularity of yam compared to the major food crop maize (Gaiser

Oueme catchment and average observed yam (a) and maize (b) yields in the period

et al., 2010b). In contrast to yam, where the bulk of the production

1998–2004.

is produced by specialized yam farmers and in most cases planted

in the ﬁrst year after fallow, maize is cropped by the bulk of the

bush savannah is available as fallow), one district (Tchaourou) falls

population at variable rank in the rotation. This explains that maize

under Scenario 2 (i.e., 50% of the bush savannah is available as fal-

yield depends strongly on the fallow–cropland ratio in this region,

low) and one district (Pehounco) falls under Scenario 3 (i.e., 25% of

whereas yam yields are, as long as land reserves are accessible to

the bush savannah is available as fallow). The fact that in this selec-

the yam farmers, still independent of the fallow–cropland ratio.

tion procedure most of the districts fall under Scenario 1 (i.e., 100%

of the bush savannah is available for yam cultivation) conﬁrms that

3.2. Estimation of fallow availability for yam per district

As Fig. 5a and Table 5 show, there are two districts (Péhounco 100% fallow availability

and Tchaourou) where the simulated yields overestimate yam pro-

−1 50% fallow availability

ductivity by more than 0.5 Mg ha . In those districts it seems that )

-1 5

fallow availability and hence fallow–cropland ratio has been over- 25 % fallow availabliity

estimated. In Tchaourou overestimation of fallow availability is

most likely due to the fact that this district which has the high-

est proportion of protected forest among the districts within the 4

catchment (46.2%). Thus, the access for the yam farmers is not

only limited by lack of infrastructure, but also by legal restric-

tions. Therefore, if yam yields at Tchaourou are simulated under

scenario 2 (50% of fallow availability), simulated yields are closer ha (Mg yield Simulated

−1 y = 0.91 x + 0.19

to the observed yield (−0.36 Mg ha ). In this way, for each dis-

R2 = 0.48

trict and scenario the simulated yam yields were compared with

the observed yield. Assuming that fallow availability is the deter- 2

minant driver for yam yield, we chose the simulated yield values of 2 3 4 5 6

yam closest to the 1:1 line under different scenarios of fallow avail-

Observed tub er yield (Mg ha-1)

ability (Fig. 5) and plotted them in Fig. 8 to identify the actual fallow

availability in each of the districts. Out of 10 districts considered in

Fig. 8. Selection of the fallow scenario for each of the 10 districts where simulated

−1

the present study, 8 districts fall under scenario 1 (i.e., 100% of the values ﬁt best to the yield level (in Mg ha ) reported in the statistics.

A.K. Srivastava et al. / Field Crops Research 131 (2012) 32–39 39

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XULU-Framework. Ph.D. Thesis, Faculty of Mathematics and Natural Sciences,

yam tuber yields. The EPIC model integrated into the SDSS PEDRO

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