The effects of management history and landscape position on inter-field variation in soil fertility and millet yields in southwestern Niger

Agriculture, Ecosystems and Environment 211 (2015) 73–83

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee

The effects of management history and landscape position on inter-field variation in soil fertility and millet yields in southwestern Niger Matthew D. Turnera,* , Pierre Hiernauxb a b

Department of Geography, 160 Science Hall, 550 N. Park Street, University of Wisconsin, Madison, WI 53706, USA Géosciences Environnement Toulouse (CNRS), 14 Avenue Edouard Belin, 31400 Toulouse, France

A R T I C L E I N F O

A B S T R A C T

Article history: Received 29 September 2014 Received in revised form 23 May 2015 Accepted 26 May 2015 Available online xxx

The effects of human management on the inter-field variability of soil fertility and crop yields were studied in southwestern Niger. Chemical fertility (organic C, total N, available P, K, exchangeable bases, pH) of the surface soils of 181 fields dispersed within two agropastoral territories covering 412 km2 was measured. Analyses of crop yields demonstrated that inter-field variation in chemical fertility more significantly affected yields than the topographic position of fields or the physical properties of their soils (soil depth and texture). Regression analysis of soil fertility parameters showed that chemical fertility is generally more influenced by the distance from nearest village than by topographic position or soil physical properties. Soil fertility declines with distance from nearest village in a negative curvilinear fashion consistent with previous work describing infield and outfield patterns of soil fertility. However, when recent management variables for monitored fields were included in regression models (average surface fraction of field that was manured over previous eight years and number of years cropped over previous twelve years), average manuring fraction was found to better predict field-level soil fertility variation than distance to nearest village for all macronutrients except phosphorus. This illustrates the importance of contemporary patterns of livestock presence and manuring across agropastoral landscapes—patterns that are not solely oriented spatially with respect to proximity to permanent villages but to pastoral encampments, livestock corridors and water points. The availability of phosphorus, a conservative nutrient within the soil system, more reflects historic nutrient inputs which are higher as one approaches villages. Therefore, phosphorus availability remains negatively correlated with distance to nearest village even when contemporary management variables are included in regression models. Soil pH shows a more complex pattern being positively affected by manure inputs but negatively associated with the duration of cultivation. Implications for commonly-used assessments of nutrient budgets and landscape-scale fertility gradients (infield—outfield model) are discussed. ã2015 Elsevier B.V. All rights reserved.

Keywords: African drylands Agropastoral landscapes Indigenous management of soils Heterogeneity of soil fertility

1. Introduction Low inherent soil fertility in Sudano-Sahelian West Africa has long been seen as the major constraint for agricultural development (Pieri, 1989; Breman et al., 2001; Sanchez, 2002). Moreover, nutrient balance studies have led to arguments that smallholder farmers in the region are trapped in a downward spiral of reduced fallowing and crop productivity declines (Powell and MohamedSaleem, 1987; van Keulen and Breman, 1990; Drechsel et al., 2001; de Rouw and Rajot, 2004). Despite these nutrient balance

* Corresponding author. Tel.: +1 608 2622465. E-mail addresses: [email protected] (M.D. Turner), [email protected] (P. Hiernaux). http://dx.doi.org/10.1016/j.agee.2015.05.010 0167-8809/ ã 2015 Elsevier B.V. All rights reserved.

assessments, other studies point to the long-term persistence of the productivity of cropped fields within the region (Krogh, 1997; Harris, 1998; Niemeijer and Mazzucato, 2002; Mortimore and Harris, 2005). Nutrient gains and losses are not uniform across time and space and therefore the scale of assessment will affect studies of soil fertility maintenance (Krogh, 1997; Scoones and Toulmin, 1998; Stoorvogel and Smaling, 1998; Schlecht and Hiernaux, 2003). To inform these debates, there is a strong need for empirical work that characterizes the spatial variation in the chemical fertility of soils under different histories of management within spatially-coherent agropastoral territories (Reenberg and Fog, 1995). Prior research on the effect of farming practices on the spatial variation of soil fertility has generally focused either on fertility

74

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83

variation within cropped fields (e.g., Brouwer et al., 1993; Rockstrom and de Rouw, 1997; Manlay et al., 2002; Gandah et al., 2003; Voortman et al., 2004) or along transects across the agropastoral territory—the area not only encompassing village fields but also the rangeland used by village-based livestock. This latter body of work has shown that fields closer to the village are more likely to be more permanently cultivated receiving net nutrient transfers from outlying areas (Pélissier, 1966; van Keulen and Breman, 1990; Prudencio, 1993; Piters and Fresco, 1995; Loireau, 1998; Niemeijer and Mazzucato, 2002; Ramisch, 2005; Samake et al., 2005). The nutrient transfers to support permanent cropping of infields are generated from intentional management decisions (transport of house wastes; transport of manure from livestock pens; corralling of livestock on the field to capture feces and urine; and transport of crop residues and coppices) and unintentional nutrient capture due to their proximity to the village

and its wells. Improved understanding of the role of management on soil fertility requires research on fertility variation between fields (managed by different farming families) at similar distances from the village center. This is the scale at which variation in the history of farming practices (fallowing, crop residue management, crop choices, tillage) as well as nutrient inputs (inorganic fertilizer, manure, green manuring, livestock corralling, and manuring by proximity to water points and pastoral encampments) could lead to inter-field variation in chemical fertility. Previous work at this scale has been dominated by on-farm studies where researchers have performed controlled studies with subplot treatments on farmers’ fields (e.g., Agbenin, 1998; Voortman et al., 2004; Dandois Dutordoir, 2006; Zingore et al., 2007) or comparisons of a small number (15) of farmer-managed fields (Krogh, 1997; Harris, 1998; de Rouw and Rajot, 2004; Ramisch, 2005). Given the high spatial variability of soil characteristics, the

Fig. 1. Map of the two agropastoral territories of this study (1 and 2) showing major topographic units and the location of villages, mapped fields, and fields where soil samples were collected.

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83

relatively small sample size of these studies severely limits their ability to characterize inter-field variability beyond showing differences between infields and outfields (e.g., differences in fields close and far from homestead or village). Studies covering a larger number of fields are much less common and are dominated by those that either do not gather information about the fields’ prior management (e.g., Wezel and Boecker, 1998; Mtambanengwe and Mapfumo, 2005; Waswa et al., 2013) or those that focused on distance from village or fallow duration as sole management variables (Niemeijer and Mazzucato, 2002; Samake et al., 2005; Tittonell et al., 2013). An exception is the work of Gray (1999, 2005),) who found that cropping and manuring history had little effect on macronutrient variation across 54 fields in the sub-humid zone of Burkina Faso. This study addresses the limits of prior work on the effect of management on soil fertility variation by focusing on inter-field variation in chemical fertility of soils controlling for distance from the nearest village and soil type. The chemical characteristics (organic C, macronutrients, base cations, pH) of pooled aggregate surface soil samples from 181 georeferenced fields were measured within two adjacent agropastoral territories in southwestern Niger. The causes of the observed inter-field variation in soil fertility were analyzed using soil mapping (topography, geomorphology, topsoil texture), measures of field distance from nearest villages, and household level interviews on field management history. The effect of soil fertility variation on crop yield was also assessed through the monitoring of harvests from a subsample of fields. 2. Materials and methods To measure the effect of human management on landscapelevel variation in the chemical fertility of fields, the following data were collected within the study area: (1) cropped fields within the two agropastoral areas (Fig.1) were mapped and incorporated into a GIS; (2) all households farming at least one of the mapped fields within the two agropastoral areas were identified. Of these, 96 households were selected with 1–3 fields farmed by each, hence a total of 181 fields selected for soil sampling; (3) two composite soil samples were obtained from the sample fields; (4) the yields of a subsample of fields (109) were collected over three consecutive years (2009–2011); and (5) the cropping and manuring histories of the sampled fields were gathered through interviews of farmers. Following a brief description of the study area, the procedures followed to procure these data are described below.

75

2.1. Study area The study was conducted within the Fakara region of southwestern Niger—an area dominated by flat-topped plateaus of cenozoic sandstone dissected by a web of shallow valleys connecting to the Dallol Boboye (Bosso), a fossil tributary of the Niger River lying to the east (Fig. 1). The long-term mean annual rainfall is 575 mm, which falls during a single rainy season from May to October. The landscape can be divided into five topographic categories: plateau, upper slope (or plateau skirt), mid-to-lower slope, mid-to-lower flat and valley (Fig. 2). Each of these categories is composed of particular sets of land forms on which particular soil types prevail (Table 1). Overall, the fertility of the sandy soils that dominate the study area is low with low pH and organic matter content (Hiernaux and Turner, 2002). The resident population is composed of Djerma millet farmers, who are long-term residents, and FulBe pastoralists who are more recent residents (since the 1970s). Settlement patterns consist of Djerma villages with FulBe homesteads located outside of Djerma villages on their loaned fields often adjacent to livestock paths. The study area is a contiguous 412 km2 portion of the Fakara and contains two agropastoral territories (Fig. 1). These two territories share a common history and have similar rainfall, soil conditions, and ethnic composition. Each territory encloses not only the croplands of Djerma villages and associated cultivation hamlets but also the rangeland utilized by the livestock of Djerma villagers and surrounding FulBe homesteads. Wells used by people and livestock are generally located near permanent villages although there are some wells, ephemeral ponds and boreholes located away from villages. Despite their common history, the two agropastoral territories experience significantly different levels of cultivation pressure with 32 and 64% of the surface area of territory 1 and territory 2 respectively being cropped. Crop agriculture, as practiced by virtually all households (Djerma and FulBe), is primarily rain-fed millet (intercropped by some with cowpea and/or sorrel) without the use of plows. Declines in the productivity of land have been described (Hiernaux et al., 2009) with farmers emphasizing soil fertility maintenance as a major priority (Warren et al., 2003). Due to its relatively high cost, inorganic fertilizer is rarely used but when used, is only applied as spoonfuls in seed pockets (micro-dose) (Osbahr, 2001). Therefore, animal manure is the main nutrient amendment that is broadcast across the field surface (and therefore has the potential of affecting field-wide soil fertility values). Manure is generally applied through corralling of livestock herds on fields although some transport of manure and application on fields is performed,

Fig. 2. Toposequence showing major topographic units of the study area's landscape along the transect delineated in Fig. 1.

76

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83

Table 1 Major landforms, surface soil texture and soil types for each of the five topographic compononents of the Fakara landscape. The dominant (in bold) and next most important landform and associated soil texture and type encountered within topography components are presented. Topography

Landform

Texture of surface soils

Soil type

Plateau

Iron pan/sandstone outcrop Loamy or sandy deposit

Rock, loamy sands Loamy sands, sands

Skeletic leptosol Ferralic arenosol

Upper slope

Thick sand deposits Gullies, colluvium fan

Sands Sands, Loamy sands

Ferralic arenosol Cambic arenosol

Mid-to- lower slopes

Sand, loamy sand deposits Depressions filled with colluvium

Sands, loamy sands Loamy sands, sands

Arenic lixisol Arenic cambisol

Mid-to- lower flats

Indurated erosive surfaces Thin deposit on flats

Gravels, loamy sands Loamy sands, gravels

Leptic lixisol Ferralic arenosol

Valley

Flood plain, levees River bed

Sands, leached Clayed loams

Gleyic arenosol Arenic gleysol

particularly to those fields closer to villages (Gandah et al., 2003). Within any given year, the quantity of manure available to a farmer is generally not sufficient to be applied over the whole field. Green manuring is generally not performed in the study area although farmers will strategically apply crop residues or coppices to trap dust and stimulate biological activity over crusted soils and bare patches (Osbahr, 2001). Fields are generally open to grazing of crop residue following harvest. Fields can therefore benefit from unmanaged nutrient inputs due to their location within the agropastoral territory. Fields closer to villages, water points, and livestock corridors benefit from manure deposition due to the greater densities of livestock (waiting near or moving to and from village/well). Fields near villages also receive greater amounts of human waste. 2.2. Mapping of crop fields This work benefited from prior fieldwork and classification of aerial photography (Hiernaux and Ayantunde, 2004) that created GIS layers of topography and land form, water points, villages, water channels or wadis (with only intermittent flows), foot and donkey cart paths, livestock corridors, and bush/tree hedges that often exist between adjacent fields (dominated by Guiera senegalensis and Combretum glutinosum). These features were printed on flat maps which were used in the field with local informants to identify boundaries between fields within each of the two agropastoral territories. The boundaries of fields were drawn on these flat maps and these boundaries were digitized (approximately 1900 fields mapped with surface areas with a median area equal to 0.7 ha). Table 2 presents data on how mapped field area within the study area (Fig. 1) corresponds to topographic units. Cropped field area is less prevalent on plateaus and valleys/mid-tolower flats and more prevalent on mid-to-lower slopes compared to the availability of these topographic units within the study area.

2.3. Soil sampling and analysis from crop fields All households within each of the two agropastoral territories have been surveyed on a recurrent basis about their composition and productive assets (livestock and land wealth) starting in 1995. A random sample of 96 households stratified across broad categories of land and livestock wealth (3  3 levels) was selected. 1–3 fields were randomly selected from those farmed by each sampled household resulting in a total sample of 181 fields (114 in territory 1 and 67 in territory 2). As shown in Table 2, these fields together cover 16 km2 of land surface with the sampled fields under-representing all mapped field coverage on plateaus (0.13 of sampled area compared to 0.17 of all mapped field area) while over-representing all mapped field coverage on mid-to-lower slopes (0.62 of sample area compared to 0.58 of all mapped area). At the end of the dry season of 2008 (early June), soil samples were collected from each sampled field along two 200 m transects— situated parallel to the long axis of the field at 10 cm depth every 4 meters. A single pooled sample for each transect was analyzed (two lab replicates per pooled sample). Soil pH was measured using a glass electrode in distilled water or 1 M KCl with a 1:2.5 soil:solution ratio. Ground samples (to pass through a 0.25 mm sieve were analyzed for organic carbon (Walkley-Black), total nitrogen (micro-Kjeldhal), available phosphorus (Bray-1 P), nitrate N, ammonium N, exchangeable acidity H+ and Al3+ (KCl extraction), and exchangeable bases (silver–thiourea extraction followed by atomic absorption spectrometry for Ca2+ and Mg2+and flame emission spectrometry for K+ and Na+) using standard methods (van Reeuwijk, 1993; Houba et al., 1995). Particle size analysis was performed for one of the pooled transect samples (one sample per field) using a combination sieving and pipette method to determine weight fractions of sand (50–2000 mm), silt (2–50 mm) and clay (<2 mm).

Table 2 Area fractions of the study area, the mapped fields and the sampled fields categorized by topographic position. In addition, the fraction of the sampled fields categorized by their major topographic position is presented. Fraction of area or fields

Study area (412 km2) Mapped fields (193 km2) Sampled field area (16 km2) Sampled fields with majority topography category (n = 181)

Plateau

Upper slopes

Mid-to-Lower slopes

Valleys and mid-to-lower flats

0.25 0.17 0.13 0.13

0.11 0.11 0.12 0.10

0.40 0.58 0.62 0.67

0.24 0.14 0.13 0.09

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83

2.4. Crop yields of sampled fields In 2009, farmers were asked whether they would be willing to have their harvests weighed within each sampled field. The farmers of 72 of the 181 sampled fields either refused to have their harvests sampled or fallowed their fields during the 2009–2011 period For the remaining 109 fields, the grain yields (dry weight of millet grain heads in grams per m2) within approximately 40 m2 plots established in the middle of the field were measured. The average grain yields over 2009–2011 are used as measures of the crop productivity of the field. While yields are expressed in dry weight of millet grain heads per m2, grain was removed from a subsample (n = 68) of harvested millet heads across the 2009–2011 period with a strong linear relationship between millet head and millet grain yield found (grain yield = 0.55*millet head yield, R2 = 0.98, n = 68). 2.5. Management history of sampled fields The management histories of sampled fields were collected through interviews of farmers in 2008. These interviews focused on estimating the year when the field was originally cleared; cropping history (crop, fallows . . . etc.) during the previous 12 years prior to soil sampling (1997–2008); and estimates of the fraction of the field that was manured over the eight years prior to soil sampling (2001–2008). The number of years from 1997–2008 that the field was cropped was used as a measure of recent cropping history. For manuring history, an average manuring fraction was calculated from estimates by the informant of the fraction of the field manured for each year within the 2001–2008 period. In some cases, we were not able to gain sufficiently precise information about prior field management due to shifts in field managers and incomplete memories. This is particularly the case for the year when the field was originally cleared which could be estimated reliably for only 107 fields. 2.6. Data analysis The relative importance of the between-field (rather than between transect) component of total variation was estimated using standard ANOVA for the 362 samples. Correlation among soil parameters was performed using simple correlation analysis and principal components analysis (PCA). PCA scores as well as key soil parameters (macronutrients) were used as dependent and independent variables in subsequent analyses. Analyses were focused on determining the relationship between the field location (soil texture, depth, topography, and distance to nearest village), field management (twelve-year

77

cropping history, eight-year manuring history, and date of field clearance) on soil chemistry parameters. As described above, soil texture was determined for a pooled sample from each field. A field’s topographic position was assigned by the topographic unit (plateau, upper slope, mid-to-lower slope, mid-to-lower flat, and valley) that covers the largest fraction of the field. Based on this rule, there were few fields assigned to the mid-to-lower flat (n = 12) or valley (n = 6) categories so these were combined into a single topographic category for analysis. In spite of the diversity of soil texture and depth, combining mid-to-lower flat and valley soils makes sense as they both receive run-on water and may be flooded seasonally, which induces leaching and acidification. Surface soil depth is variable across topographic units and was categorized into relatively thick (>0.2 m) and thin (<0.2 m) soils based on which soil depth covers the majority of the field’s area. Given that the management data are incomplete (see Table 3), a staged approach was followed with respect to regression analyses: Stage 1. Regression of soil parameters on field location variables (181 fields); Stage 2. Regression of soil parameters on field location and cropping/manuring history variables (149 fields); and Stage 3. Regression of soil parameters on field location and all three management variables (107 fields). Regressions were also performed evaluating the effect of soil parameters (soil texture, depth, chemistry, and topography) on three-year average crop yields for a subsample of sampled fields (109 fields). For all regression analyses, standard errors were adjusted for the nonindependence of the two transects within fields (robust estimation of standard errors clustered by field ID). Residuals of all regression models were analyzed to ensure constancy of variance, independence, normality of error terms, and lack of collinearity. Only models that do not violate these conditions were used. 3. Results Table 4 presents the mean, standard deviation, minimum and maximum of the soil parameters measured across 362 samples (181 fields  2 transects). Soil parameters reflect the generally low fertility status of farmers’ fields within the study area with considerable spatial variability. In all cases, the macronutrient (N, P, K) and micronutrient (Mg, Ca, Na) concentrations range by at least a factor of ten. One-way ANOVAs show that a high fraction of the observed spatial variability (>80%) is between fields rather than between transects within fields. There is a high degree of correlation among soil parameters (pH, organic C, N, P, and K and soil texture values) with all bivariate correlation coefficients significant at the 5% level (except for K with soil texture parameters). Total N and organic C, two important soil fertility parameters, are strongly correlated (r = 0.95). Organic C

Table 3 Variables used in regression analyses. The number of fields for which data for these variables is available is provided. Variable description

Variable Name

# Of fields

Soil chemical analyses (listed in Table 4) Topographic position of field (plateau, upper slope, mid-to-lower slope, valley/mid-to-lower flat)

(Variable) Plateau(0–1), Upper slope (0–1), Valley/flat (0–1) Thin (0–1) Sand%, Clay%

181 181

Top soil depth of the field (thin (2 m) and thick (>2 m) Soil texture of the soil sampled (% of soil being sand, silt, or clay)

181 181 181

Distance (km) to nearest village Year when field was reported to be first cleared (1900..) Number of years field was cultivated from 1997–2008 (0–12) Average % of field manured over 8 years (2001–2008)

DistVil ClearYr CropYrs AvgMan%8

107 149 149

Average crop yield of field over the 2009–2011 period (grams of millet heads per m2)

AvgCrpYield

109

78

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83

Table 4 The mean, standard deviation (SD), minimum, and maximum of the soil analyses of 362 pooled samples collected along two transects within 181 fields located within two agropastoral territories in the Fakara region of western Niger. The percentage of total variance that is inter-field variance (%IntFlds) is estimated by one-way ANOVAs of each parameter on field ID. Parameter

Mean

SD

Min

Max

%IntFlds

pH_H2O (1:2.5) pH_KCl (1:2.5) Total N (mg N/kg) Organic carbon (%) Bray P1 (mg P/kg) NH4+ (mg N/kg) NO3- (mg N/kg) EB_H (cmol + /kg) EB_Al (cmol + /kg) EB_Na (cmol + /kg) EB_K (cmol + /kg) EB_Ca (cmol + /kg) EB_Mg (cmol + /kg) Total Exchangeable Bases (TEB) (cmol + /kg) Al saturation% Sand % (2000–50 mm) Silt % (50–2 mm) Clay % (<2 mm)

5.46 4.69 148.86 0.18 5.11 2.71 7.92 0.05 0.03 0.05 0.20 0.36 0.22 0.83 3.32 94.30 2.60 3.09

0.38 0.42 65.44 0.07 3.74 2.34 3.90 0.03 0.05 0.03 0.12 0.20 0.11 0.37 5.74 2.78 1.31 1.57

3.54 3.61 60.48 0.09 2.26 0.25 0.10 0.01 0.00 0.01 0.03 0.02 0.04 0.28 0.00 76.78 0.94 1.72

6.84 6.57 697.54 0.78 50.75 19.44 19.75 0.22 0.38 0.46 1.08 2.03 1.10 4.17 38.75 96.92 9.68 13.91

89 87 84 82 89 88 93 87 78 80 90 85 83 84 72 – – –

and macronutrient (N,P,K) fertility show a negative correlation with soils of higher sand fraction which also display higher pH values. Table 5 presents the results of principal components analysis of the soil chemical parameters including organic C, macronutrients (total_N, Bray-1 P, K), pH (H2O) and total exchangeable bases (TEB). Principal component 1, representing 64% of total variation, shows high positive loadings for all the parameters except a weak positive loading for pH. Principal component 2, representing 16% of total variation, shows a high positive loading for pH, negative loadings for nitrogen and phosphorus, and weakly positive loadings for base cations (including K). This component captures pH variation that is not associated with organic inputs or higher cation exchange capacity (TEB). Principal component 3, representing 9% of total variation, shows a high positive loading for Bray-1 P and negative loadings for other parameters except for pH which is weakly positive. Principal component 4, representing 7% of total variation, shows high positive loadings for base cations (including K) but negative loadings for other parameters except for Bray-1 P which is weakly positive. These results point to PC1 as the best overall measure of chemical fertility of soils with other components representing sources of variation where one or more parameters vary independently from other chemical variables (pH for PC2, Bray-1 P for PC3, and base cations for PC4).

Table 5 The results of principal components analysis of macronutrient values (Total N, organic C, and Bray-1 P, exchangeable K), pH, and total exchangeable bases for the 362 composite soil samples in the 181 sampled fields. For each principal component, the loadings for each fertility variable and the proportion of total variance are provided. Variable

PC1

pH (H2O) Total_N Organic C Bray-1 P Exchangeable K TEB Prop. of Variance

0.17 0.47 0.48 0.36 0.40 0.48

0.92 0.17 0.18 0.25 0.15 0.15

0.23 0.20 0.14 0.89 0.26 0.26

0.21 0.31 0.32 0.15 0.85 0.85

0.15 0.40 0.27 0.04 0.18 0.18

0.01 0.67 0.74 0.05 0.03 0.03

0.64

0.16

0.09

0.07

0.02

0.01

PC2

PC3

PC4

PC5

PC6

3.1. The effect of fertility variation on crop yield Regression analysis of average yields (grams of millet grain heads per m2) of the 109 fields where crop yields were measured during 2009–2011 shows that soil texture, chemistry, and topographic position explain 25% of the observed yield variation across 109 fields (Table 6). This relatively low R2 reflects the fact that yields were not controlled for rainfall received by fields nor management practices such as millet variety, planting density and weeding. Topography, soil depth and soil texture are found to have less influence on yield than soil chemistry. The principal component scores of chemical fertility show strong positive (PC1 at p = 0.001) and marginally-significant negative (PC2 at p = 0.09 and PC3 at p = 0.05) effects on crop yield. Once controlled for soil chemistry, soil texture has stronger effects on yield compared to soil depth and topography. Higher sand and clay percentages have marginally negative (p = 0.08) and positive (p = 0.09) effects respectively on millet yield. 3.2. The effect of field location on soil fertility Fertility of soils varies with topography and soil texture, and, as discussed above, it is also influenced by the distance of the field Table 6 Results of least-squares regression analysis of 2009–2011 average crop yields (g/m2) of monitored fields (AvgCrpYield) on geomorphic position (plateau, valley/flat, upper slope), soil depth (thin soil), soil texture (sand%, clay%), and soil chemical fertility (PC1–PC4) using cluster robust standard error estimation (number of clusters = 109). For each independent variable, the regression coefficient (coef), t statistic (t), and significance level (p) are given. The overall regression is significant with F statistic (10, 108) = 2.5, p = 0.01 and a R2 of 0.25. Variable Plateau Valley/ flat Upper slope Sand% Clay% Thin Soil PC1 PC2 PC3 PC4 Constant

Coef 7.07 9.40 4.44 12.92 6.48 6.04 10.97 5.07 3.61 7.47 576.40

T

p 0.92 1.38 0.39 1.77 1.71 1.18 3.49 1.72 1.96 1.19 1.56

0.36 0.17 0.70 0.08 0.09 0.24 0.001 0.09 0.05 0.24 0.12

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83

79

Table 7 The results of regression models investigating the effects of landscape position on the variation of macronutrient fertility (Total_N, Org_C, Bray-1 P, and exchangeable K), total exchangeable bases (TEB), pH (H2O) and PC1-3 scores of composite soil samples collected in 181 sampled fields using cluster robust standard error estimation (N = 362, 181 clusters). Model parameters are presented including: dependent variable (dependent), the F-statistic (7, 180), the coefficient of determination (R2), and the coefficients of independent variables and their significancea based on t-statistic. Landscape position variables include those for topographic position of the field (dummy variables for plateau, upper slope, and valley/flat), soil texture (percentage sand and clay), and distance of field to nearest village (dist_vil, dist_vil2). Model # Dependent

1 Total_N

F-Statistic R2 Constant Plateau Upper slope Valley/flat Sand% Clay% Dist_Vil Dist_Vil2

6.46**** 0.22 1524.68 0.41* 3.12 10.35 13.62 8.56 68.93** 12.51**

2 Org_C 7.98**** 0.25 2.10* 0.01 0.01 0.00 0.02* 0.02 0.07** 0.01**

3 Bray-1 P 6.81**** 0.27 111.77* 2.44* 0.15 0.24 1.04* 1.27 4.79**** 0.88***

4 K

5 TEB 4.07*** 0.15 0.45 0.04 0.00 0.03 0.01 0.02 0.11** 0.02**

3.61** 0.11 3.86 0.08 0.00 0.06 0.03 0.01 0.31* 0.06*

6 PC1 5.59**** 0.19 36.32 0.54 0.07 0.20 0.35 0.30 2.23** 0.40**

7 PC2 42.91**** 0.32 15.34 0.18 0.04 0.05 0.16 0.08** 0.57 0.11*

8 PC3

9 pH (H2O)

4.31*** 0.10 17.39 0.43 0.02 0.18 0.17 0.34* 0.37 0.07

19.59**** 0.17 5.84 0.02 0.00 0.00 0.00 0.10 0.03 0.01

a

Significance levels. Equals p  0.05. ** Equals p  0.01. *** Equals p  0.001. **** Equals p  0.0001. *

from the nearest village. Given that the village establishment is determined in part by the topographic position (reflecting the locations for permanent wells), it is justified to evaluate the effects of these factors jointly. It is important to note that while we treat distance to nearest village, topography, soil texture of a field as independent of the farmer’s management of the field, these factors are shaped in part by farmers’ decisions. Within land tenure constraints, farmers choose where to establish fields and these choices are made in part based on the location of the field in relation to the village, soil texture and topography. Still, we must control for these locational factors in evaluating the effects of the field's management by the farmer. Table 7 presents the results of the first-stage regression analyses focussed on the effect of field location (topography, soil texture and distance to nearest village) on soil fertility variables across all 181 fields. Spatial variation in organic C, macronutrients and PC1 scores across the agropastoral territory are primarily shaped by distance from nearest village (in a negative curvilinear fashion) with few significant effects of topography or soil texture (soils on plateaus have higher total N and Bray-1 P). The R2s vary from 0.15 to 0.27. Fields closer to villages show higher PC1 scores and higher levels of organic C, N, P, K and TEB. Variation in available phosphorus (Bray-1 P) is most strongly related with distance to village. Soil pH on the other hand shows no relation to distance from village. PC2, with a high positive pH loading and negative macronutrient loadings (Table 5), shows a positive curvilinear association with distance from nearest village. Therefore, while we do not observe variation in soil pH with distance from village, the first two principal components, each with positive loadings of soil pH, have opposite relations with distance from village. 3.3. The effect of field management (cropping and manuring histories) on soil fertility A conservative approach for evaluating the effect of field management on soil fertility is to analyse the effects of cropping and manuring history while controlling for landscape position. Table 8 presents the regression results for the 149 fields for which recent management history was available (stage 2). Recent cropping history is not found to significantly affect the spatial variation in macronutrient availability. On the other hand, the addition of recent manuring history (AvgMan%8) to field location

variables significantly increases the R2s for most of the fertility parameter models (N, Organic C, TEB, K, PC1, pH) despite the smaller sample size (R2s ranging from 0.29 to 0.39). This is despite the fact that our estimated manuring rates are not measures of the actual quantity of manure applied by unit area but are averages of the manure coverage of the field each year (fraction of the field manured) as estimated by informants. Variation in the quantity of manure applied to manure-covered fields can be quite high (Gandah et al., 2003). The levels of these fertility parameters (except for K, pH, TEB) remain negatively related to distance to nearest village although the significance of the relationships declines compared to models that do not include recent management history (Table 7). It is important to note that contrary to the other fertility parameters, recent manuring rates (AvgMan%8) do not significantly influence the spatial variation in available P. Bray-1 P remains strongly negatively correlated with distance to village in a curvilinear manner. Similarly, the PC2 and PC3 scores are only weakly influenced by recent manuring rates in a positive and negative manner, respectively. The addition of ClearYr (year in which the cropped field was initially cleared) to the model (stage 3) reduced the number of fields that were included (107) and did not improve model fits (R2s ranging from 0.22 to 0.44). There were no cases where ClearYr was significant in these full models. This reflects the fact that ClearYr is significantly related to distance to village in a positive curvilinear fashion (R2 = 0.33, p < 0.0001). Moreover, it is difficult to provide precise estimates of when a field is cleared not only because of incomplete memories but because it is often cleared incrementally (expanded) over many years. ClearYr is found to be negatively associated with macronutrient availability and PC1 scores when it substitutes for DistVil and DistVil2 in the stage 2 models presented in Table 8 (R2s ranging from 0.25 to 0.38). ClearYr is found to be positively associated with PC2 (p = 0.018) which is consistent with long-term cultivation history lowering soil pH (R2 = 0.40, p < 0.0001). 4. Discussion Despite the high spatiotemporal distribution of rainfall in the study area (Minet, 2007), variation in the fertility of farmermanaged fields is found to significantly affect the millet yields of these fields over three consecutive years. While there is significant

80

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83

Table 8 The results of regression models investigating the effects of landscape position and recent management on the variation of macronutrient fertility (Total_N, Org_C, Bray-1 P, and exchangeable K), total exchangeable bases (TEB), pH (H20) and PC1-3 scores of composite soil samples collected in 149 sampled fields using cluster robust standard error estimation (N = 298, 149 clusters). Model parameters are presented including: dependent variable (dependent), the F-statistic (9, 148), the coefficient of determination (R2), coefficients of independent variables and their significancea based on t-statistic. Landscape position variables include those for topographic position of the field (dummy variables for plateau, upper slope, and valley/flat), soil texture (percentage sand and clay), and distance of field to nearest village (dist_vil). Management variables include: the number of years out of the previous 12 years that the field was cropped (CropYrs); and the average fraction of the field that was manured over the previous 8 years (AvgMan%). Model # Dependent

1 Total_N

2 Org_C

3 Bray-1 P

F-Statistic R2 Constant Plateau Upper slope Valley/flat Sand% Clay% Dist_Vil Dist_Vil2 CropYrs AvgMan%8

12.34**** 0.39 1321.35 1.45 4.79 5.64 12.02 4.32 55.72* 10.76* 0.39 102.03****

13.27**** 0.38 1.83* 0.01 0.01 0.01 0.02 0.01 0.06* 0.01* 0.00 0.10***

5.05**** 0.31 141.14 2.72* 0.47 0.17 1.33 1.72 5.38*** 0.98*** 0.09 1.93

4 K

5 TEB 7.96**** 0.36 1.08 0.04 0.01 0.01 0.01 0.03 0.06 0.01 0.00* 0.22***

6.49**** 0.29 4.77 0.07 0.01 0.00 0.04 0.01 0.22 0.05 0.00 0.65***

6 PC1 10.89**** 0.39 33.08 0.53 0.14 0.09 0.33 0.20 1.85* 0.36* 0.00 3.43****

7 PC2 31.17**** 0.33 25.69** 0.15 0.02 0.19 0.26** 0.14 0.66** 0.11* 0.01 0.72*

8 PC3

9 pH (H2O)

4.22**** 0.17 24.31* 0.52 0.11 0.08 0.23 0.46 0.72** 0.13** 0.03* 0.63*

18.29**** 0.24 2.25 0.00 0.02 0.08 0.03 0.02 0.04 0.00 0.00 0.39

a

Significance levels. Equals p  0.05. Equals p  0.01. *** Equals p  0.001. **** Equals p  0.0001. *

**

prior agroecological work showing the importance of soil nutrients on crop yields (e.g., Rockstrom and de Rouw, 1997; Buerkert et al., 2002; Samake et al., 2005; Hiernaux et al., 2009), this study is noteworthy in that it empirically links inter-field variation in chemical fertility to crop yields for a relatively large number of fields (n = 109) within contiguous village territories. Higher chemical fertility, much more than topographic position, soil depth and texture, positively influences crop yield. This finding differs from those of prior geomorphological work in the region pointing to the importance of the physical characteristics of soils in affecting crop yields (Graef et al., 1998; Rockström et al., 1999). It is consistent with understandings of local farmers who point to the importance of soil chemistry in explaining inter-field productivity differences (Warren et al., 2003). Moreover, the spatial patterns of soil chemistry are not consistent with what would be expected from studies in the area on patterns of surface soil loss (due to wind and water erosion) and spatial patterns of wind-blown dust deposition (Chappell et al., 1998; Bielders et al., 2002). The relatively limited effect of physical soil properties or topography on crop yields most likely reflects the limited range of soil texture conditions and topographic position of sampled fields (very sandy soils found on mid-to-lower slopes)—conditions though where most fields exist within the study area (Table 2). The results of staged regression analyses point to the strong influence of human farming activities on landscape-scale variability of chemical fertility in the studied village territories. Previous studies in semi-arid West Africa have found that distance to village is negatively associated with macronutrient levels in field soils (Prudencio, 1993; Niemeijer and Mazzucato, 2002; Ramisch, 2005; Samake et al., 2005). These prior findings could be questioned because the location of villages is shaped by perceived soil quality and geomorphology. Did the higher fertility of soils near villages precede the establishment of these villages or is it the result of human activities oriented around villages? This study adds further support to the later interpretation by showing that this relationship still holds when controlling for topographic position and soil texture. Human activities, which are both intentional (corralling of livestock, transport of manure and residue to nearby fields) and incidental (greater concentration of food processing, human and livestock excrement near villages), lead to a net movement of nutrients from outlying areas toward villages.

This study also shows that there remains significant variation in soil fertility once one controls for distance from village. Regression analyses, controlling for the field location (topography, soil texture, distance from village), show that recent manuring history (past 8 years) significantly increases field-level soil fertility. This is consistent with agronomic on-farm and on-research station work in the region (Powell et al., 1999; Ramisch, 1999; Esse et al., 2001; Manyame, 2006). The rate of cropping experienced by the field (previous 12 years) had no significant effect on soil fertility. This is inconsistent with the view that even short-term fallows can increase soil fertility (e.g., de Rouw and Rajot, 2004) but is consistent with other work showing mixed or small effects of shorter-term fallowing on soil fertility or vegetative productivity (Gray, 2005; Hiernaux et al., 2009). Still, interpretation of our findings is complicated by the fact that farmers are more likely to put infertile fields into fallow. Clearance year of the field, a parameter strongly correlated with distance to village in this study area, was found to be negatively associated with organic C and macronutrient levels in sampled soils. In other words, older fields are more fertile—most likely reflecting the fact that more fertile areas were put into cultivation earlier and the fact that the older fields closer to permanent settlements receive more nutrient inputs (e.g., infields). These findings differ from those of Gray (1999, 2005) whose investigation is the other large study investigating inter-field variation in soil fertility in the region. Within her study area in Burkina Faso, Gray found soil fertility to be largely affected by soil texture (stony vs. sandy) while field management parameters (such as manuring, fallowing, cropping) had no statistically significant effects on N, P, K, or organic C. The only management variable found to affect macronutrient levels in field soils was the prior use of animal traction (plowing). A range of reasons may explain these different findings. Gray’s study site has soils of higher chemical fertility making management effects more difficult to measure. Sampled fields may as well have covered a wider range of soil textures and associated differences in chemical fertility among soil types (e.g., variable clay content). Farmers reportedly use greater amounts of inorganic fertilizer (most likely broadcast applied) which may mask the effects of manuring and cropping history. The smaller number of fields and the fact that distance

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83

from village is not controlled for may also hide management effects in Gray’s regression analysis. 4.1. The infield, outfield model revisited As described earlier, much of the prior work on the spatial variability of soil fertility has adopted sampling frames based on the infield, outfield model where fields closer to the permanent village receive significant nutrient inputs and are farmed on a more continuous basis while the chemical fertility of outfields maintained through fallowing is prone to decline as land becomes scarcer (van Keulen and Breman, 1990). Researchers have generally stratified their sampling by infield and outfield categories (Niemeijer and Mazzucato, 2002; Ramisch, 2005) or have performed transects away from villages (Prudencio, 1993; Samake et al., 2005; Tittonell et al., 2013). This study partially supports this model in that we find cropping frequency to decline (r = 0.47) and year of field clearance to increase (r = 0.47) with distance from village. Moreover, once controlling for topography and soil texture, we find a statistically-significant variation in soil chemistry with distance from the nearest village. However, we find that recent manuring history more strongly shapes soil chemistry when incorporated with distance to village into regression models (Table 8). While correlated, average manuring rates are less correlated with distance to village (r = 0.27) with only 7% of manuring rate variation explained by distance to village. This finding is supported by an examination of the geography of the sampled agropastoral territories. The FulBe, who manage a large fraction of livestock in the area, especially during the rainy season, live on fields loaned to them by the Djerma along livestock corridors to facilitate the movement of their livestock (Fig. 3). These fields are heavily manured. In addition, fields near villages, livestock corridors, FulBe hamlets and waterpoints receive nutrient inputs as livestock move across them during the dry season (Turner and Hiernaux, 2002). In short, there are parallel land-use geographies not only in the study area but within agropastoral

81

territories across the Sudano-Sahelian region that result in more complicated spatial patterns of chemical fertility variation than is commonly thought. As shown in Fig. 3, average available phosphorus and total nitrogen values for fields reflect this complex geography more than simply by their location with respect to villages. 4.2. Divergent spatial gradients of fertility parameters While a number of soil parameters, particularly total N and organic C, were found to be strongly correlated across the 181 sampled fields, principal components analysis demonstrates that there are some parameters that display independent variation from others. Variable loadings and regression analyses support the conclusion that PC1 is oriented in parallel to gradients in shortterm history (<10 years) of nutrient flows (net imports to net exports). This is consistent with the relatively high positive variable loadings of total N, organic C, K, and TEB and to a lesser extent, Bray-1 P. Moreover, regression analyses found PC1 to have a significant positive relationship with recent manuring history and negative curvilinear relationship with distance from village. PC2, on the other hand, represents a dimension of variability very much oriented to pH variation unrelated to recent nutrient inputs (or PC1). Indeed, pH has a very high positive loading on PC2 with all other soil parameters displaying low loadings in either negative or positive directions (Table 5). Regression analyses show that PC2 is positively associated in a curvilinear fashion with distance to village. Thus, the spatial variation of PC2 runs counter to what one expect from the rise of pH associated with greater organic matter inputs closer to the village (PC1). This suggests countervailing effects of long-term cropping sustained through nutrient inputs as one approaches villages: nutrient availability is higher with higher organic matter content expected to have a positive influence on pH (PC1) but pH is also depressed due to a longer history of cropping (PC2). In short, there is a depressive effect on pH in situations of fields experiencing not only a longer

Fig. 3. Map of agropastoral territory 1 showing locations of villages, livestock corridors, ephemeral ponds/pans, wells, and FulBe encampments in relation to sampled fields showing variable levels of available phosphorus (A) and total nitrogen (B).

82

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83

history of cultivation but higher nutrient and carbon flow-through (both inputs and outputs are higher in fields near villages). As a result of these countervailing influences, we find pH to not vary significantly with distance from village. This observed pattern runs counter to previous studies that found higher soil pH in fields closer to villages (Prudencio, 1993; Samake et al., 2005). It is consistent with experimental agronomic work demonstrating that organic matter inputs tend to increase soil pH while a long history of cropping tends to lower pH (Pieri, 1992). While recent history of manuring (AvgMan%8), controlled for distance from village, was found to increase levels of most all soil fertility parameters (Total N, Organic C, K, TEB, pH), the spatial variability of available P was found to be uninfluenced by recent management history. Bray-1 P remains strongly related to distance to village whether or not AvgMan%8 is entered into the model or not (model 3 of Tables 7 and 8). This finding can be explained by the fact that P is a conservative nutrient in the system and therefore the longer-term history of land management has more influence on its spatial variation than it has for other soil parameters such as total N and organic C. Phosphorus availability gradients around human settlements and livestock encampment sites have been documented in the region (Turner, 1998). These gradients in soil phosphorus levels reflect the net fluxes of P aggregated over many decades rather than years. The much stronger orientation of phosphorus availability to permanent villages compared to other macronutrients also may reflect variation in the nature of nutrient inputs near villages and away. Fields closer to villages are more likely to receive human excrement and house wastes as well as manure carted in from livestock pens. Manured areas in outlying fields are more likely to be manured through the corralling of animals and therefore receive both manure and urine and thus relatively higher nitrogen inputs than fields receiving carted manure (Somda et al., 1995). Due to these differences, we would expect total nitrogen in soils to be less correlated with distance to nearest village, as we found. Still, these considerations do not explain why we find the same for organic C which should more closely reflect manure (rather than urine) inputs.

The study also reveals that there are dimensions of spatial variability beyond that of infields and outfields. Controlling for distance to nearest village, recent history (past 8 years) of manuring is found to be positively associated with the N, K, TEB, Organic C and pH status of monitored fields. The variability of phosphorus on the other hand is found to be explained best by aggregate historic patterns of nutrient transfer (distance to nearest village) rather than recent nutrient management. The strong gradient of phosphorus at agropastoral scales supports the finding that the relative importance of nitrogen and phosphorus (controlled for recent management) varies at the scale of the village territory. Moreover, the variation of soil pH is found to show a more complicated pattern with respect to distance to village resulting from the elevation of pH associated with organic matter inputs but pH depression from long-term farming. This supports the need for more attention by extension programs to the variation in fertilizer requirements across a village territory. The high heterogeneity of soil fertility, shaped significantly by human management and investment, makes the calculation of nutrient budgets very sensitive to scale. Given the importance of animal manure for maintaining soil fertility, it is appropriate that these assessments in the Sahelian region be conducted at the scale of the agropastoral territory incorporating both cropped fields and pastures. In the study area, cropped area has extended to the limits of arable land. The decline of pasture and fallowed areas could lead to less livestock presence in the study area having possible negative effects on local manure supply jeopardizing soil fertility maintenance over the long term.

5. Conclusions

References

This study found evidence for significant variation of the chemical fertility of soils among farmers’ fields in two contiguous agropastoral territories in western Niger. This variation was found to be strongly related to human management and to significantly affect crop yields. The strong role played by human management we find in this study actually underestimates the influence of management on the spatial heterogeneity of soil fertility since our focus on inter-field variability ignores the role of management in affecting within-field heterogeneity of soil fertility. Once controlled for edaphic condition (topography, soil depth and soil texture), the analyses revealed wide variation in the chemical fertility of soils associated with human-managed activities. Intentional and incidental nutrient transfers result in greater fertility of fields near villages and pastoral camps (as well as livestock paths). The observed gradient of soil fertility parameters with distance to village results from declines in the fertility of outlying fields and the maintenance or increase in fertility in fields near villages and pastoral camps. Recent cropping frequency (within last 12 years) is not found to affect the fertility of fields which calls into question the viability of current patterns of fallowing to maintain or improve soil fertility. On the other hand, soil fertility is better maintained on manured fields located across the territory either near Djerma villages, FulBe encampments or water points.

Agbenin, J.O., 1998. Quantifying soil fertility changes and degradation induced by cultivation techniques in the Nigerian savanna. Adv. Geoecol. 435–441. Bielders, C.L., Rajot, J.-L., Amadou, M., 2002. Transport of soil and nutrients by wind in bush fallow land and traditionally managed cultivated fields in the Sahel. Geoderma 109, 19–39. Breman, H., Groot, J.J.R., van Keulen, H., 2001. Resource limitations in Sahelian agriculture. Global Environ. Change 11, 59–68. Brouwer, J., Fussel, L.K., Hermann, L., 1993. Soil and crop growth variability in the West-African semi-arid tropics: a possible risk-reducing factor for subsistence farmers, Agriculture. Ecosyst. Environ. 45, 229–238. Buerkert, A., Piepho, H.-P., Bationo, A., 2002. Multi-site time-trend analysis of soil fertility management effects on crop production in sub-Saharan West Africa. Exp. Agric. 38, 163–183. Chappell, A., Warren, A., Taylor, N., Charlton, M., 1998. Soil flux (loss and gain) in southwestern Niger and its agricultural impact. Land Degrad. Dev. 9, 295–310. Dandois Dutordoir, C., 2006. Impact de pratiques de gestion de la fertilité sur les rendements en mil dans le Fakara (Niger). Faculté D’ingénierie Biologique, Agronomique Et Environnementale. Université Catholique de Louvain, Louvainla-Neuve, Belgium. de Rouw, A., Rajot, J.L., 2004. Nutrient availability and pearl millet production in Sahelian farming systems based on manuring or fallowing. Agric. Ecosys. Environ. 104, 249–262. Drechsel, P., Kunze, D., Penning de Vries, F., 2001. Soil nutrient depletion and population growth in Sub-Saharan Africa: a Malthusian nexus? Popul. Environ. 22, 411–423. Esse, P.C., Buerkert, A., Hiernaux, P., Assa, A., 2001. Decomposition of and nutrient release from ruminant manure on acid sandy soils inthe Sahelian zone of Niger, West Africa. Agric. Ecosyst. Environ. 83, 55–63. Gandah, M., Bouma, J., Brouwer, J., Hiernaux, P., van Duivenbooden, N., 2003. Strategies to optimize allocation of limited nutrients to sandy soils of the Sahel: a case study from Niger, West Africa. Agric. Ecosyst. Environ. 94, 311–319.

Acknowledgements This research was supported by the National Science Foundation (award # 648075) and by the French research project ESCAPE: Environmental and Social Changes in Africa: past, present and future (ANR-10-CEPL-005). In addition, the research benefits from the previous work in the study site performed by the International Livestock Research Institute (ILRI) in Niger. We would like to thank Omar Moumouni and Adamou Kalilou for their contributions to the research.

M.D. Turner, P. Hiernaux / Agriculture, Ecosystems and Environment 211 (2015) 73–83 Graef, F., van Duivenbooden, N., Stahr, K., 1998. Remote sensing- and transect-based retrieval of spatial soil and terrain (SOTER) information in semi-arid Niger. J. Arid Environ. 39, 631–644. Gray, L.C., 1999. Is land being degraded? A multi-scale investigation of landscape change in southwestern Burkina Faso. Land Degrad. Dev. 10, 329–343. Gray, L.C., 2005. What kind of intensification? Agricultural practice, soil fertility, and socioeconomic differentiation in rural Burkina Faso. Geog. J. 171, 70–82. Harris, F.M.A., 1998. Farm-level assessment of the nutrient balance in northern Nigeria. Agric. Ecosyst. Environ. 71, 201–214. Hiernaux, P., Ayantunde, A., Kalilou, A., Mougin, E., Gerard, B., Baup, F., Grippa, M., Djaby, B., 2009. Trends in productivity of crops, fallow and rangelands in Southwest Niger: impact of land use, management and variable rainfall. J. Hydrol. 375, 65–77. Hiernaux, P., Ayantunde, A.A., 2004. The Fakara: a semi-arid agro-ecosystem under stress. report of research activities. First Phase (July 2002-June of the DMP-GEF Program (GEF/2711-02-4516). International Livestock Research Institute, Niamey, Niger, pp. 2004. Hiernaux, P., Turner, M.D., 2002. The influence of farmer and pastoralist management practices on desertification processes in the Sahel. In: Reynolds, J. F., Smith, M.S. (Eds.), Global Desertification. Do Humans Cause Deserts? Dahlem University Press, Berlin, pp. 135–148. Houba, V.J.G., van der Lee, J.J., Novozamsky, I., 1995. Part 5B: Soil Analysis Procedures: Other procedures. In: Novozamski, I., van der Lee, J.J., Houba, V.J.G., Walinga, I., Van Vark, W., Temminghoff, E. (Eds.), Soil and Plant Analyses. Department of Soil Science and Plant Nutrition, Wageningen Agricultural University, Wageningen, The Netherlands. Krogh, L., 1997. Field and village nutrient balances in millet cultivation in northern Burkina Faso: a village case study. J. Arid Environ. 35, 147–159. Loireau, M., 1998. Espaces-ressources-usages: spatialisation des interactions dynamiques entre les systemes écologiques au sahel nigérien. Département de Géographie. Université Paul Valery- Montpellier III, Montpellier. Manlay, R.J., Chotte, J.-L., Masse, D., Laurent, J.-Y., Feller, C., 2002. Carbon, nitrogen and phosphorus allocation in agro-ecosystems of a West African savanna. III Plant and soil components under continuous cultivation. Agric. Ecosyst. Environ. 88, 249–269. Manyame, C., 2006. On-farm yield and water use response of pearl millet to different management practices in niger. Soil Science. Texas A&M University, College Station, Texas. Minet, J., 2007. Influence de la dispersion du parcellaire sur la gestion du risque climatique au Fakara, Niger. Faculté d’Ingénierie Biologique, Agronomique et Environnementale. Université Catholique de Louvain, Louvain-la-Neuve, Belgium. Mortimore, M., Harris, F., 2005. Do small farmers’ achievements contradict the nutrient depletion scenarios for Africa? Land Use Policy 22, 43–56. Mtambanengwe, F., Mapfumo, P., 2005. Organic matter management as an underlying cause for soil fertility gradients on smallholder farms in Zimbabwe. Nutr. Cycling Agroecosyst. 73, 227–243. Niemeijer, D., Mazzucato, V., 2002. Soil degradation in West African Sahel: how serious is it? Environment 44, 20–33. Osbahr, H., 2001. Livelihood stategies and soil fertility at Fandou Beri, southwestern Niger. Department of Geography. University College, London, London. Pélissier, P., 1966. Les paysans du Sénégal. Les civilisations agraire du cayor à la casamance. Fabrègue, Saint-Yrieix (Haute-Vienne), France. Pieri, C., 1989. Fertilité des terres de savanes. bilan de trente années de recherche et de développement agricole au sud du sahara. Ministère de la Coopération et du Développement and Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Paris. Pieri, C., 1992. Fertility of Soils. A Future for the Farming in the West African Savanna. New York, Springer-Verlag. Piters, B.D.S., Fresco, L.O., 1995. Farmers managing their most scarce natural resource: an example of local level soil fertility management in Northern Cameroon. In: PBreemer, J.P.M., Drijver, C.A., Venema, L.B. (Eds.), Local Resource Management in Africa. John Wiley and Sons, New York. Powell, J.M., Ikpe, F.N., Somda, Z.C., 1999. Crop yield and the fate of nitrogen and phosphorus following application of plant material and feces to soil. Nutr. Cycling Agroecosyst. 54, 215–226.

83

Powell, J.M., Mohamed-Saleem, M.A., 1987. Nitrogen and phosphorus transfers in a crop-livestock system in West Africa. Agric. Syst. 25, 261–277. Prudencio, C.F., 1993. Ring management of soils and crops in the West Africa semiarid tropics: the case of the Mossi farming system in Burkina Faso. Agric. Ecosyst. Environ. 47, 237–264. Ramisch, J., 1999. In the balance? Evaluating soil nutrient budgets for an agropastoral village of southern Mali. Managing Africa's Soils 9. Drylands Programme, International Institute for Environment and Development, United Kingdom. Ramisch, J.J., 2005. Inequality, agro-pastoral exchanges, and soil fertility gradient in southern Mali. Agric. Ecosyst. Environ. 105, 353–372. Reenberg, A., Fog, B., 1995. The spatial pattern and dynamics of a Sahelian agroecosystem—land use systems analysis combining household survey and georelated information. GeoJournal 37, 489–499. Rockström, J., Barron, J., Brouwer, J., Galle, S., de Rouw, A., 1999. On-farm spatial and temporal variability of soil and water in pearl millet cultivation. Soil Sci. Soc. Am. J. 63, 1308–1319. Rockstrom, J., de Rouw, A., 1997. Water, nutrient and slope position in on-farm pearl millet cultivation in the Sahel. Plant Soil 311–327. Samake, O., Smaling, E.M.A., Kropff, M.J., Stomph, T.J., Kodio, A., 2005. Effects of cultivation practices on spatial variation of soil fertility and millet yields in the Sahel of Mali. Agric. Ecosyst. Environ. 109, 335–345. Sanchez, P.A., 2002. Soil fertility and hunger in Africa. Science 295/5562, 2019–2020. Schlecht, E., Hiernaux, P., 2003. The scale issues in assessing matter fluxes and balance in/out agro-ecosystems. Conference on International Agricultural Research for Development, 8–10 October, Deutscher Tropentag, Göttingen, Germany. Scoones, I., Toulmin, C., 1998. Soil nutrient balances: what use for policy. Agric. Ecosyst. Environ. 71, 255–267. Somda, Z.C., Powell, J.M., Fernandez-Riviera, S., Reed, J., 1995. Feed factors affecting nutrient excretion by ruminants and fate of nutrients when applied to soil. In: Powell, J.M., Fernandez-Rivera, S., Williams, T.O., Renard, C. (Eds.), Livestock and Sustainable Nutrient Cycling in Mixed Farming Systems of sub-Saharan Africa. Proceedings of an International Conference, International Livestock Centre for Africa, Addis Ababa, Ethiopia, 22–26 November 1993, pp. 227–243. Stoorvogel, J.J., Smaling, E.M.A., 1998. Research on soil fertility decline in tropical environments: integration of spatial scales. Nutr. Cycling Agroecosyst. 50, 151–158. Tittonell, P., Muriuki, A., Klapwijk, C.J., Shepherd, K.D., Coe, R., Vanlauwe, B., 2013. Soil heterogeneity and soil fertility gradients in smallholder farms of the East African highlands. Soil Sci. Soc. Am. J. 77, 525–538. Turner, M.D., 1998. Long-term effects of daily grazing orbits on nutrient availability in Sahelian West Africa I. Gradients in the chemical composition of rangeland soils and vegetation. J. Biogeogr. 25, 669–682. Turner, M.D., Hiernaux, P., 2002. The use of herders’ accounts to map livestock activities across agropastoral landscapes in Semi-Arid Africa. Landscape Ecol. 17, 367–385. van Keulen, H., Breman, H., 1990. Agricultural development in the West African Sahelian region: a cure against land hunger. Agric. Ecosyst. Environ. 32, 177–197. van Reeuwijk, L.P., 1993. Procedures for Soil Analyses. International Soil Reference and Information Centre Wageningen, The Netherlands. Voortman, R.L., Brouwer, J., Albersen, P.J., 2004. Characterization of spatial soil variability and its effect on millet yield on Sudano-Sahelian coversands in SW Niger. Geoderma 121, 65–82. Warren, A., Osbahr, H., Batterbury, S., Chappell, A., 2003. Indigenous views of soil erosion at Fandou Beri, southwestern Niger. Geoderma 111, 439–456. Waswa, B.S., Vlek, P.L.G., Tamene, L.D., Okoth, P., Mbakaya, D., Zingore, S., 2013. Evaluating indicators of land degradation in smallholder farming systems of western Kenya. Geoderma 195–196, 192–200. Wezel, A., Boecker, R., 1998. Fallow plant communities and site characteristics in semi-arid Niger, West Africa. J. Arid Environ. 40, 269–280. Zingore, S., Murwira, H.K., Delve, R.J., Giller, K.E., 2007. Soil type, management history and current resource allocation: Three dimensions regulating variability in crop productivity on African smallholder farms. Field Crops Res. 101, 296–305.